TY  - CONF
AU  - Ralić, Vanja
AU  - Gemović, Branislava
AU  - Nešić, Maja D.
AU  - Popović, Iva
AU  - Stepić, Milutin
AU  - Petković, Marijana
PY  - 2023
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/12390
AB  - In this work, we have applied the Informational Spectrum Method (ISM) to discover a potential protein target in the HeLa cervical cancer cell line for [Pd(dach)Cl2] complex. Since Pd complexes are considered an alternative to traditionally used Pt complexes in anti-cancer therapy, it is essential to elucidate the mechanism of their action. A detailed analysis that also involves screening the known protein databases revealed the proteins of the SOSS complex as the most probable [Pd(dach)Cl2] targets. Since this protein maintains genomic stability, this result shows the potential of the Pd(II) complex as an anti-cancer drug.
PB  - Kragujevac : Institute for Information Technologies, University of Kragujevac
C3  - ICCBIKG 2023 : 2nd International Conference on Chemo and Bioinformatics : Book of Proceedings
T1  - Identification of protein target molecules for [Pd(dach)Cl2] complex in HeLa cervical carcinoma cells
SP  - 296
EP  - 299
DO  - 10.46793/ICCBI23.296R
ER  - 

@conference{
author = "Ralić, Vanja and Gemović, Branislava and Nešić, Maja D. and Popović, Iva and Stepić, Milutin and Petković, Marijana",
year = "2023",
abstract = "In this work, we have applied the Informational Spectrum Method (ISM) to discover a potential protein target in the HeLa cervical cancer cell line for [Pd(dach)Cl2] complex. Since Pd complexes are considered an alternative to traditionally used Pt complexes in anti-cancer therapy, it is essential to elucidate the mechanism of their action. A detailed analysis that also involves screening the known protein databases revealed the proteins of the SOSS complex as the most probable [Pd(dach)Cl2] targets. Since this protein maintains genomic stability, this result shows the potential of the Pd(II) complex as an anti-cancer drug.",
publisher = "Kragujevac : Institute for Information Technologies, University of Kragujevac",
journal = "ICCBIKG 2023 : 2nd International Conference on Chemo and Bioinformatics : Book of Proceedings",
title = "Identification of protein target molecules for [Pd(dach)Cl2] complex in HeLa cervical carcinoma cells",
pages = "296-299",
doi = "10.46793/ICCBI23.296R"
}

Ralić, V., Gemović, B., Nešić, M. D., Popović, I., Stepić, M.,& Petković, M.. (2023). Identification of protein target molecules for [Pd(dach)Cl2] complex in HeLa cervical carcinoma cells. in ICCBIKG 2023 : 2nd International Conference on Chemo and Bioinformatics : Book of Proceedings
Kragujevac : Institute for Information Technologies, University of Kragujevac., 296-299.
https://doi.org/10.46793/ICCBI23.296R

Ralić V, Gemović B, Nešić MD, Popović I, Stepić M, Petković M. Identification of protein target molecules for [Pd(dach)Cl2] complex in HeLa cervical carcinoma cells. in ICCBIKG 2023 : 2nd International Conference on Chemo and Bioinformatics : Book of Proceedings. 2023;:296-299.
doi:10.46793/ICCBI23.296R .

Ralić, Vanja, Gemović, Branislava, Nešić, Maja D., Popović, Iva, Stepić, Milutin, Petković, Marijana, "Identification of protein target molecules for [Pd(dach)Cl2] complex in HeLa cervical carcinoma cells" in ICCBIKG 2023 : 2nd International Conference on Chemo and Bioinformatics : Book of Proceedings (2023):296-299,
https://doi.org/10.46793/ICCBI23.296R . .

TY  - CONF
AU  - Ralić, Vanja
AU  - Nešić, Maja
AU  - Abu el Rub, Anamarija
AU  - Dučić, Tanja
AU  - Gemović, Branislava
AU  - Algarra, Manuel
AU  - Stepić, Milutin
AU  - Korićanac, Lela
AU  - Žakula, Jelena
AU  - Petković, Marijana
PY  - 2023
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/12657
AB  - Nanocomposite system formulated from surface-modified S-doped carbon dot (S-CD) nanoparticle with a potential metallodrug, palladium(II) complex, dichloro(1,2-diaminocyclohexane)palladium(II), [Pd(dach)Cl2] (Pd@S-CD), was investigated as a model system for the treatment of cervical carcinoma (HeLa) cells. To examine the intracellular biochemical effects induced by the Pd@S-CD, we used Synchrotron Radiation-based Fourier-transform infrared spectroscopy (SR FTIR). SR FTIR spectroscopy was employed to investigate the alterations in cellular components’ biochemical composition and secondary structure upon exposure to Pd@S-CD. Spectral analysis, complemented by statistical techniques, revealed changes in biomolecules, lipids, proteins, nucleic acids, and carbohydrates caused by the treatment with Pd@CDs. These results and the increased cytotoxicity of the system demonstrate its high anti-cervical cancer therapeutic potential.
PB  - Kragujevac : Institute for Information Technologies, University of Kragujevac
C3  - ICCBIKG 2023 : 2nd International Conference on Chemo and Bioinformatics : Book of Proceedings
T1  - SR FTIR spectroscopy investigation of Pd@S-CD nanocomposite system effects on biomolecules in cervical carcinoma cells
SP  - 467
EP  - 470
DO  - 10.46793/ICCBI23.467R
ER  - 

@conference{
author = "Ralić, Vanja and Nešić, Maja and Abu el Rub, Anamarija and Dučić, Tanja and Gemović, Branislava and Algarra, Manuel and Stepić, Milutin and Korićanac, Lela and Žakula, Jelena and Petković, Marijana",
year = "2023",
abstract = "Nanocomposite system formulated from surface-modified S-doped carbon dot (S-CD) nanoparticle with a potential metallodrug, palladium(II) complex, dichloro(1,2-diaminocyclohexane)palladium(II), [Pd(dach)Cl2] (Pd@S-CD), was investigated as a model system for the treatment of cervical carcinoma (HeLa) cells. To examine the intracellular biochemical effects induced by the Pd@S-CD, we used Synchrotron Radiation-based Fourier-transform infrared spectroscopy (SR FTIR). SR FTIR spectroscopy was employed to investigate the alterations in cellular components’ biochemical composition and secondary structure upon exposure to Pd@S-CD. Spectral analysis, complemented by statistical techniques, revealed changes in biomolecules, lipids, proteins, nucleic acids, and carbohydrates caused by the treatment with Pd@CDs. These results and the increased cytotoxicity of the system demonstrate its high anti-cervical cancer therapeutic potential.",
publisher = "Kragujevac : Institute for Information Technologies, University of Kragujevac",
journal = "ICCBIKG 2023 : 2nd International Conference on Chemo and Bioinformatics : Book of Proceedings",
title = "SR FTIR spectroscopy investigation of Pd@S-CD nanocomposite system effects on biomolecules in cervical carcinoma cells",
pages = "467-470",
doi = "10.46793/ICCBI23.467R"
}

Ralić, V., Nešić, M., Abu el Rub, A., Dučić, T., Gemović, B., Algarra, M., Stepić, M., Korićanac, L., Žakula, J.,& Petković, M.. (2023). SR FTIR spectroscopy investigation of Pd@S-CD nanocomposite system effects on biomolecules in cervical carcinoma cells. in ICCBIKG 2023 : 2nd International Conference on Chemo and Bioinformatics : Book of Proceedings
Kragujevac : Institute for Information Technologies, University of Kragujevac., 467-470.
https://doi.org/10.46793/ICCBI23.467R

Ralić V, Nešić M, Abu el Rub A, Dučić T, Gemović B, Algarra M, Stepić M, Korićanac L, Žakula J, Petković M. SR FTIR spectroscopy investigation of Pd@S-CD nanocomposite system effects on biomolecules in cervical carcinoma cells. in ICCBIKG 2023 : 2nd International Conference on Chemo and Bioinformatics : Book of Proceedings. 2023;:467-470.
doi:10.46793/ICCBI23.467R .

Ralić, Vanja, Nešić, Maja, Abu el Rub, Anamarija, Dučić, Tanja, Gemović, Branislava, Algarra, Manuel, Stepić, Milutin, Korićanac, Lela, Žakula, Jelena, Petković, Marijana, "SR FTIR spectroscopy investigation of Pd@S-CD nanocomposite system effects on biomolecules in cervical carcinoma cells" in ICCBIKG 2023 : 2nd International Conference on Chemo and Bioinformatics : Book of Proceedings (2023):467-470,
https://doi.org/10.46793/ICCBI23.467R . .

TY  - JOUR
AU  - Nešić, Maja D.
AU  - Dučić, Tanja
AU  - Gonçalves, Mara
AU  - Stepić, Milutin
AU  - Algarra, Manuel
AU  - Soto, Juan
AU  - Gemović, Branislava S.
AU  - Bandosz, Teresa J.
AU  - Petković, Marijana
PY  - 2022
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/10267
AB  - Carbon dots (CDs) and N-carbon dots (N-CDs) loaded with Ru-complex (CDs@RuCN, N-CDs@RuCN, respectively) were investigated as media imposing biochemical changes induced by UV illumination of ovarian cancer, A2780, and osteosarcoma, CAL72, cells. Synchrotron radiation-based Fourier Transform Infrared Spectroscopy was performed, and the spectra were subjected to a Principal Component Analysis. The CDs@RuCN and N-CDs@RuCN effects on cancer cells were analyzed by the theoretical modelling of the stability of the composite systems and a protein database search. Moreover, a detailed evaluation of surface and optical properties of CDs@RuCN and N-CDs@RuCN was carried out. Results demonstrated selective action of the CDs@RuCN and N-CDs@RuCN-based photodynamic therapy, with N-CDs@RuCN being the most active in inducing changes in A2780 and CDs@RuCN in CAL72 cells. We assume that different surface charges of nanoparticles led to direct interactions of N-CDs@RuCN with a Wnt signalling pathway in A2780 and those of CDs@RuCN with PI3–K/Akt in CAL72 cells and that further biochemical changes occurred upon light illumination.
T2  - Chemico-Biological Interactions
T1  - Biochemical changes in cancer cells induced by photoactive nanosystem based on carbon dots loaded with Ru-complex
VL  - 360
SP  - 109950
DO  - 10.1016/j.cbi.2022.109950
ER  - 

@article{
author = "Nešić, Maja D. and Dučić, Tanja and Gonçalves, Mara and Stepić, Milutin and Algarra, Manuel and Soto, Juan and Gemović, Branislava S. and Bandosz, Teresa J. and Petković, Marijana",
year = "2022",
abstract = "Carbon dots (CDs) and N-carbon dots (N-CDs) loaded with Ru-complex (CDs@RuCN, N-CDs@RuCN, respectively) were investigated as media imposing biochemical changes induced by UV illumination of ovarian cancer, A2780, and osteosarcoma, CAL72, cells. Synchrotron radiation-based Fourier Transform Infrared Spectroscopy was performed, and the spectra were subjected to a Principal Component Analysis. The CDs@RuCN and N-CDs@RuCN effects on cancer cells were analyzed by the theoretical modelling of the stability of the composite systems and a protein database search. Moreover, a detailed evaluation of surface and optical properties of CDs@RuCN and N-CDs@RuCN was carried out. Results demonstrated selective action of the CDs@RuCN and N-CDs@RuCN-based photodynamic therapy, with N-CDs@RuCN being the most active in inducing changes in A2780 and CDs@RuCN in CAL72 cells. We assume that different surface charges of nanoparticles led to direct interactions of N-CDs@RuCN with a Wnt signalling pathway in A2780 and those of CDs@RuCN with PI3–K/Akt in CAL72 cells and that further biochemical changes occurred upon light illumination.",
journal = "Chemico-Biological Interactions",
title = "Biochemical changes in cancer cells induced by photoactive nanosystem based on carbon dots loaded with Ru-complex",
volume = "360",
pages = "109950",
doi = "10.1016/j.cbi.2022.109950"
}

Nešić, M. D., Dučić, T., Gonçalves, M., Stepić, M., Algarra, M., Soto, J., Gemović, B. S., Bandosz, T. J.,& Petković, M.. (2022). Biochemical changes in cancer cells induced by photoactive nanosystem based on carbon dots loaded with Ru-complex. in Chemico-Biological Interactions, 360, 109950.
https://doi.org/10.1016/j.cbi.2022.109950

Nešić MD, Dučić T, Gonçalves M, Stepić M, Algarra M, Soto J, Gemović BS, Bandosz TJ, Petković M. Biochemical changes in cancer cells induced by photoactive nanosystem based on carbon dots loaded with Ru-complex. in Chemico-Biological Interactions. 2022;360:109950.
doi:10.1016/j.cbi.2022.109950 .

Nešić, Maja D., Dučić, Tanja, Gonçalves, Mara, Stepić, Milutin, Algarra, Manuel, Soto, Juan, Gemović, Branislava S., Bandosz, Teresa J., Petković, Marijana, "Biochemical changes in cancer cells induced by photoactive nanosystem based on carbon dots loaded with Ru-complex" in Chemico-Biological Interactions, 360 (2022):109950,
https://doi.org/10.1016/j.cbi.2022.109950 . .

TY  - JOUR
AU  - Kuiper, Martin
AU  - Bonello, Joseph
AU  - Fernández-Breis, Jesualdo T.
AU  - Bucher, Philipp
AU  - Futschik, Matthias E.
AU  - Gaudet, Pascale
AU  - Kulakovskiy, Ivan V.
AU  - Licata, Luana
AU  - Logie, Colin
AU  - Lovering, Ruth C.
AU  - Makeev, Vsevolod J.
AU  - Orchard, Sandra
AU  - Panni, Simona
AU  - Perfetto, Livia
AU  - Sant, David
AU  - Schulz, Stefan
AU  - Vercruysse, Steven
AU  - Zerbino, Daniel R.
AU  - Lægreid, Astrid
PY  - 2022
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/10768
AB  - As computational modeling becomes more essential to analyze and understand biological regulatory mechanisms, governance of the many databases and knowledge bases that support this domain is crucial to guarantee reliability and interoperability of resources. To address this, the COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC, CA15205, www.greekc.org) organized nine workshops in a four-year period, starting September 2016. The workshops brought together a wide range of experts from all over the world working on various steps in the knowledge management process that focuses on understanding gene regulatory mechanisms. The discussions between ontologists, curators, text miners, biologists, bioinformaticians, philosophers and computational scientists spawned a host of activities aimed to standardize and update existing knowledge management workflows and involve end-users in the process of designing the Gene Regulation Knowledge Commons (GRKC). Here the GREEKC consortium describes its main achievements in improving this GRKC.
T2  - Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms
T1  - The gene regulation knowledge commons: the action area of GREEKC
VL  - 1865
IS  - 1
SP  - 194768
DO  - 10.1016/j.bbagrm.2021.194768
ER  - 

@article{
author = "Kuiper, Martin and Bonello, Joseph and Fernández-Breis, Jesualdo T. and Bucher, Philipp and Futschik, Matthias E. and Gaudet, Pascale and Kulakovskiy, Ivan V. and Licata, Luana and Logie, Colin and Lovering, Ruth C. and Makeev, Vsevolod J. and Orchard, Sandra and Panni, Simona and Perfetto, Livia and Sant, David and Schulz, Stefan and Vercruysse, Steven and Zerbino, Daniel R. and Lægreid, Astrid",
year = "2022",
abstract = "As computational modeling becomes more essential to analyze and understand biological regulatory mechanisms, governance of the many databases and knowledge bases that support this domain is crucial to guarantee reliability and interoperability of resources. To address this, the COST Action Gene Regulation Ensemble Effort for the Knowledge Commons (GREEKC, CA15205, www.greekc.org) organized nine workshops in a four-year period, starting September 2016. The workshops brought together a wide range of experts from all over the world working on various steps in the knowledge management process that focuses on understanding gene regulatory mechanisms. The discussions between ontologists, curators, text miners, biologists, bioinformaticians, philosophers and computational scientists spawned a host of activities aimed to standardize and update existing knowledge management workflows and involve end-users in the process of designing the Gene Regulation Knowledge Commons (GRKC). Here the GREEKC consortium describes its main achievements in improving this GRKC.",
journal = "Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms",
title = "The gene regulation knowledge commons: the action area of GREEKC",
volume = "1865",
number = "1",
pages = "194768",
doi = "10.1016/j.bbagrm.2021.194768"
}

Kuiper, M., Bonello, J., Fernández-Breis, J. T., Bucher, P., Futschik, M. E., Gaudet, P., Kulakovskiy, I. V., Licata, L., Logie, C., Lovering, R. C., Makeev, V. J., Orchard, S., Panni, S., Perfetto, L., Sant, D., Schulz, S., Vercruysse, S., Zerbino, D. R.,& Lægreid, A.. (2022). The gene regulation knowledge commons: the action area of GREEKC. in Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms, 1865(1), 194768.
https://doi.org/10.1016/j.bbagrm.2021.194768

Kuiper M, Bonello J, Fernández-Breis JT, Bucher P, Futschik ME, Gaudet P, Kulakovskiy IV, Licata L, Logie C, Lovering RC, Makeev VJ, Orchard S, Panni S, Perfetto L, Sant D, Schulz S, Vercruysse S, Zerbino DR, Lægreid A. The gene regulation knowledge commons: the action area of GREEKC. in Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms. 2022;1865(1):194768.
doi:10.1016/j.bbagrm.2021.194768 .

Kuiper, Martin, Bonello, Joseph, Fernández-Breis, Jesualdo T., Bucher, Philipp, Futschik, Matthias E., Gaudet, Pascale, Kulakovskiy, Ivan V., Licata, Luana, Logie, Colin, Lovering, Ruth C., Makeev, Vsevolod J., Orchard, Sandra, Panni, Simona, Perfetto, Livia, Sant, David, Schulz, Stefan, Vercruysse, Steven, Zerbino, Daniel R., Lægreid, Astrid, "The gene regulation knowledge commons: the action area of GREEKC" in Biochimica et Biophysica Acta (BBA) - Gene Regulatory Mechanisms, 1865, no. 1 (2022):194768,
https://doi.org/10.1016/j.bbagrm.2021.194768 . .

TY  - JOUR
AU  - Drljača, Tamara
AU  - Zukić, Branka
AU  - Kovačević, Vladimir
AU  - Gemović, Branislava S.
AU  - Karan-Đurašević, Teodora
AU  - Perović, Vladimir R.
AU  - Lazarević, Mladen
AU  - Pavlović, Sonja
AU  - Veljković, Nevena V.
PY  - 2021
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/10769
AB  - The complete understanding of the genomic contribution to complex traits, diseases, and response to treatments, as well as genomic medicine application to the well-being of all humans will be achieved through the global variome that encompasses fine-scale genetic diversity. Despite significant efforts in recent years, uneven representation still characterizes genomic resources and among the underrepresented European populations are the Western Balkans including the Serbian population. Our research addresses this gap and presents the first ever targeted sequencing dataset of variants in clinically relevant genes. By measuring population differentiation and applying the Principal Component and Admixture analysis we demonstrated that the Serbian population differs little from other European populations, yet we identified several novel and more frequent variants that appear as its unique genetic determinants. We explored thoroughly the functional impact of frequent variants and its correlation with the health burden of the population of Serbia based on a sample of 144 individuals. Our variants catalogue improves the understanding of genetics of modern Serbia, contributes to research on ancestry, and aids in improvements of well-being and health equity. In addition, this resource may also be applicable in neighboring regions and valuable in worldwide functional analyses of genetic variants in individuals of European descent.
T2  - Scientific Reports
T1  - The first insight into the genetic structure of the population of modern Serbia
VL  - 11
IS  - 1
SP  - 13995
DO  - 10.1038/s41598-021-93129-4
ER  - 

@article{
author = "Drljača, Tamara and Zukić, Branka and Kovačević, Vladimir and Gemović, Branislava S. and Karan-Đurašević, Teodora and Perović, Vladimir R. and Lazarević, Mladen and Pavlović, Sonja and Veljković, Nevena V.",
year = "2021",
abstract = "The complete understanding of the genomic contribution to complex traits, diseases, and response to treatments, as well as genomic medicine application to the well-being of all humans will be achieved through the global variome that encompasses fine-scale genetic diversity. Despite significant efforts in recent years, uneven representation still characterizes genomic resources and among the underrepresented European populations are the Western Balkans including the Serbian population. Our research addresses this gap and presents the first ever targeted sequencing dataset of variants in clinically relevant genes. By measuring population differentiation and applying the Principal Component and Admixture analysis we demonstrated that the Serbian population differs little from other European populations, yet we identified several novel and more frequent variants that appear as its unique genetic determinants. We explored thoroughly the functional impact of frequent variants and its correlation with the health burden of the population of Serbia based on a sample of 144 individuals. Our variants catalogue improves the understanding of genetics of modern Serbia, contributes to research on ancestry, and aids in improvements of well-being and health equity. In addition, this resource may also be applicable in neighboring regions and valuable in worldwide functional analyses of genetic variants in individuals of European descent.",
journal = "Scientific Reports",
title = "The first insight into the genetic structure of the population of modern Serbia",
volume = "11",
number = "1",
pages = "13995",
doi = "10.1038/s41598-021-93129-4"
}

Drljača, T., Zukić, B., Kovačević, V., Gemović, B. S., Karan-Đurašević, T., Perović, V. R., Lazarević, M., Pavlović, S.,& Veljković, N. V.. (2021). The first insight into the genetic structure of the population of modern Serbia. in Scientific Reports, 11(1), 13995.
https://doi.org/10.1038/s41598-021-93129-4

Drljača T, Zukić B, Kovačević V, Gemović BS, Karan-Đurašević T, Perović VR, Lazarević M, Pavlović S, Veljković NV. The first insight into the genetic structure of the population of modern Serbia. in Scientific Reports. 2021;11(1):13995.
doi:10.1038/s41598-021-93129-4 .

Drljača, Tamara, Zukić, Branka, Kovačević, Vladimir, Gemović, Branislava S., Karan-Đurašević, Teodora, Perović, Vladimir R., Lazarević, Mladen, Pavlović, Sonja, Veljković, Nevena V., "The first insight into the genetic structure of the population of modern Serbia" in Scientific Reports, 11, no. 1 (2021):13995,
https://doi.org/10.1038/s41598-021-93129-4 . .

TY  - JOUR
AU  - Gemović, Branislava S.
AU  - Perović, Vladimir R.
AU  - Davidović, Radoslav S.
AU  - Drljača, Tamara
AU  - Veljković, Nevena V.
PY  - 2021
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/8894
AB  - For the last couple of decades, there has been a significant growth in sequencing data, leading to an extraordinary increase in the number of gene variants. This places a challenge on the bioinformatics research community to develop and improve computational tools for functional annotation of new variants. Genes coding for epigenetic regulators have important roles in cancer pathogenesis and mutations in these genes show great potential as clinical biomarkers, especially in hematologic malignancies. Therefore, we developed a model that specifically focuses on these genes, with an assumption that it would outperform general models in predicting the functional effects of amino acid substitutions. EpiMut is a standalone software that implements a sequence based alignment-free method. We applied a two-step approach for generating sequence based features, relying on the biophysical and biochemical indices of amino acids and the Fourier Transform as a sequence transformation method. For each gene in the dataset, the machine learning algorithm–Naïve Bayes was used for building a model for prediction of the neutral or disease-related status of variants. EpiMut outperformed state-of-the-art tools used for comparison, PolyPhen-2, SIFT and SNAP2. Additionally, EpiMut showed the highest performance on the subset of variants positioned outside conserved functional domains of analysed proteins, which represents an important group of cancer-related variants. These results imply that EpiMut can be applied as a first choice tool in research of the impact of gene variants in epigenetic regulators, especially in the light of the biomarker role in hematologic malignancies. EpiMut is freely available at https://www.vin.bg.ac.rs/180/tools/epimut.php.
T2  - PLOS One
T1  - Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies
VL  - 16
IS  - 1
SP  - e0244948
DO  - 10.1371/journal.pone.0244948
ER  - 

@article{
author = "Gemović, Branislava S. and Perović, Vladimir R. and Davidović, Radoslav S. and Drljača, Tamara and Veljković, Nevena V.",
year = "2021",
abstract = "For the last couple of decades, there has been a significant growth in sequencing data, leading to an extraordinary increase in the number of gene variants. This places a challenge on the bioinformatics research community to develop and improve computational tools for functional annotation of new variants. Genes coding for epigenetic regulators have important roles in cancer pathogenesis and mutations in these genes show great potential as clinical biomarkers, especially in hematologic malignancies. Therefore, we developed a model that specifically focuses on these genes, with an assumption that it would outperform general models in predicting the functional effects of amino acid substitutions. EpiMut is a standalone software that implements a sequence based alignment-free method. We applied a two-step approach for generating sequence based features, relying on the biophysical and biochemical indices of amino acids and the Fourier Transform as a sequence transformation method. For each gene in the dataset, the machine learning algorithm–Naïve Bayes was used for building a model for prediction of the neutral or disease-related status of variants. EpiMut outperformed state-of-the-art tools used for comparison, PolyPhen-2, SIFT and SNAP2. Additionally, EpiMut showed the highest performance on the subset of variants positioned outside conserved functional domains of analysed proteins, which represents an important group of cancer-related variants. These results imply that EpiMut can be applied as a first choice tool in research of the impact of gene variants in epigenetic regulators, especially in the light of the biomarker role in hematologic malignancies. EpiMut is freely available at https://www.vin.bg.ac.rs/180/tools/epimut.php.",
journal = "PLOS One",
title = "Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies",
volume = "16",
number = "1",
pages = "e0244948",
doi = "10.1371/journal.pone.0244948"
}

Gemović, B. S., Perović, V. R., Davidović, R. S., Drljača, T.,& Veljković, N. V.. (2021). Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies. in PLOS One, 16(1), e0244948.
https://doi.org/10.1371/journal.pone.0244948

Gemović BS, Perović VR, Davidović RS, Drljača T, Veljković NV. Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies. in PLOS One. 2021;16(1):e0244948.
doi:10.1371/journal.pone.0244948 .

Gemović, Branislava S., Perović, Vladimir R., Davidović, Radoslav S., Drljača, Tamara, Veljković, Nevena V., "Alignment-free method for functional annotation of amino acid substitutions: Application on epigenetic factors involved in hematologic malignancies" in PLOS One, 16, no. 1 (2021):e0244948,
https://doi.org/10.1371/journal.pone.0244948 . .

TY  - CONF
AU  - Perović, Vladimir
AU  - Gemović, Branislava S.
AU  - Veljković, Nevena V.
PY  - 2021
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/11014
AB  - Progress in a wide range of fields ranging from population genetics to precision medicine may be attributed to availability of big biological data. Alignment-free sequence comparison is the methodology of choice in data-intensive applications given that it is significantly faster and requires less resources compared to traditional sequence comparison based on pairwise or multiple sequence alignment. The symbiosis of alignment-free methods with machine learning is a paradigm of new age in bioinformatics, as it ensures the much needed boost to quicken the complex predictions on large datasets, particularly of molecules with low sequence identity. In this talk, I will present two stories in which I will describe approaches to predict functional consequences of gene variants and imperfect tandem repeats in protein sequences.
PB  - Department of Biology and Ecology : Faculty of Sciences University of Novi Sad
C3  - Biologia Serbica : Belgrade BioInformatics Conference : BelBi2021 : program and the book of abstracts; June 21-25
T1  - New age for alignment-free methods for sequence analyses
SP  - 49
UR  - https://hdl.handle.net/21.15107/rcub_vinar_11014
ER  - 

@conference{
author = "Perović, Vladimir and Gemović, Branislava S. and Veljković, Nevena V.",
year = "2021",
abstract = "Progress in a wide range of fields ranging from population genetics to precision medicine may be attributed to availability of big biological data. Alignment-free sequence comparison is the methodology of choice in data-intensive applications given that it is significantly faster and requires less resources compared to traditional sequence comparison based on pairwise or multiple sequence alignment. The symbiosis of alignment-free methods with machine learning is a paradigm of new age in bioinformatics, as it ensures the much needed boost to quicken the complex predictions on large datasets, particularly of molecules with low sequence identity. In this talk, I will present two stories in which I will describe approaches to predict functional consequences of gene variants and imperfect tandem repeats in protein sequences.",
publisher = "Department of Biology and Ecology : Faculty of Sciences University of Novi Sad",
journal = "Biologia Serbica : Belgrade BioInformatics Conference : BelBi2021 : program and the book of abstracts; June 21-25",
title = "New age for alignment-free methods for sequence analyses",
pages = "49",
url = "https://hdl.handle.net/21.15107/rcub_vinar_11014"
}

Perović, V., Gemović, B. S.,& Veljković, N. V.. (2021). New age for alignment-free methods for sequence analyses. in Biologia Serbica : Belgrade BioInformatics Conference : BelBi2021 : program and the book of abstracts; June 21-25
Department of Biology and Ecology : Faculty of Sciences University of Novi Sad., 49.
https://hdl.handle.net/21.15107/rcub_vinar_11014

Perović V, Gemović BS, Veljković NV. New age for alignment-free methods for sequence analyses. in Biologia Serbica : Belgrade BioInformatics Conference : BelBi2021 : program and the book of abstracts; June 21-25. 2021;:49.
https://hdl.handle.net/21.15107/rcub_vinar_11014 .

Perović, Vladimir, Gemović, Branislava S., Veljković, Nevena V., "New age for alignment-free methods for sequence analyses" in Biologia Serbica : Belgrade BioInformatics Conference : BelBi2021 : program and the book of abstracts; June 21-25 (2021):49,
https://hdl.handle.net/21.15107/rcub_vinar_11014 .

TY  - CONF
AU  - Grbić, Milana
AU  - Gemović, Branislava
AU  - Davidović, Radoslav
AU  - Kartelj, Aleksandar
AU  - Matić, Dragan
PY  - 2021
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/11017
AB  - Partitioning large biological networks can help biologists to retrieve new information for particular biological structures. In literature, various methods for partitioning and clustering biological networks have been proposed. The aim of such a network partitioning is to retrieve smaller structures which are easier to analyse, but still containing important information about relations between the network elements. Highly connected deletion problem is one of such network partitioning, with the aim to partition a network into highly connected components (hcd components) by deleting minimum number of edges. A network component with n nodes is a hcd component if the degree of every vertex is larger than n/2. For the purpose of this research, we used a specially constructed local search based heuristic approach to identify hcd components. Dealing with protein-protein interaction (PPI) networks, it has been noticed that proteins from the same hcd component in a network have same Gene Ontology (GO) annotations. Based on that, we proposed a new method for prediction of GO annotations, which consists of the following steps: (a) starting PPI network is partitioned to hcd components; (b) the obtained hcd components are expanded by proteins which became singletons in the partition set; (c) the newly formed extended hcd components are the subject of further enrichment analysis in DiNGO tool, which returns a list of existing GO terms for proteins from the considered extended component; (d) after propagation through GO hierarchy, the extended list of GO is obtained; (e) each protein from the extended hcd component is annotated by a number of GO terms obtained from the previous step; The proposed method is tested on the data from CAFA-3 challenge. Comparing the F1-measure of the obtained results, a combination of parameters (type of extension, cutoff for enrichment analysis and maximum number of GO terms) with the best performances is selected for the further usage. The method with the selected parameters was further applied on a class of Intrinsically Disordered Proteins (IDP). Preliminary results indicate that this method can be useful for proposing new GO terms for IDP proteins.
PB  - Department of Biology and Ecology : Faculty of Sciences University of Novi Sad
C3  - Biologia Serbica : Belgrade BioInformatics Conference : BelBi2021 : program and the book of abstracts; June 21-25
T1  - Prediction of GO terms for IDPs based on highly connected components in PPI networks
VL  - 43
IS  - 1
SP  - 112
UR  - https://hdl.handle.net/21.15107/rcub_vinar_11017
ER  - 

@conference{
author = "Grbić, Milana and Gemović, Branislava and Davidović, Radoslav and Kartelj, Aleksandar and Matić, Dragan",
year = "2021",
abstract = "Partitioning large biological networks can help biologists to retrieve new information for particular biological structures. In literature, various methods for partitioning and clustering biological networks have been proposed. The aim of such a network partitioning is to retrieve smaller structures which are easier to analyse, but still containing important information about relations between the network elements. Highly connected deletion problem is one of such network partitioning, with the aim to partition a network into highly connected components (hcd components) by deleting minimum number of edges. A network component with n nodes is a hcd component if the degree of every vertex is larger than n/2. For the purpose of this research, we used a specially constructed local search based heuristic approach to identify hcd components. Dealing with protein-protein interaction (PPI) networks, it has been noticed that proteins from the same hcd component in a network have same Gene Ontology (GO) annotations. Based on that, we proposed a new method for prediction of GO annotations, which consists of the following steps: (a) starting PPI network is partitioned to hcd components; (b) the obtained hcd components are expanded by proteins which became singletons in the partition set; (c) the newly formed extended hcd components are the subject of further enrichment analysis in DiNGO tool, which returns a list of existing GO terms for proteins from the considered extended component; (d) after propagation through GO hierarchy, the extended list of GO is obtained; (e) each protein from the extended hcd component is annotated by a number of GO terms obtained from the previous step; The proposed method is tested on the data from CAFA-3 challenge. Comparing the F1-measure of the obtained results, a combination of parameters (type of extension, cutoff for enrichment analysis and maximum number of GO terms) with the best performances is selected for the further usage. The method with the selected parameters was further applied on a class of Intrinsically Disordered Proteins (IDP). Preliminary results indicate that this method can be useful for proposing new GO terms for IDP proteins.",
publisher = "Department of Biology and Ecology : Faculty of Sciences University of Novi Sad",
journal = "Biologia Serbica : Belgrade BioInformatics Conference : BelBi2021 : program and the book of abstracts; June 21-25",
title = "Prediction of GO terms for IDPs based on highly connected components in PPI networks",
volume = "43",
number = "1",
pages = "112",
url = "https://hdl.handle.net/21.15107/rcub_vinar_11017"
}

Grbić, M., Gemović, B., Davidović, R., Kartelj, A.,& Matić, D.. (2021). Prediction of GO terms for IDPs based on highly connected components in PPI networks. in Biologia Serbica : Belgrade BioInformatics Conference : BelBi2021 : program and the book of abstracts; June 21-25
Department of Biology and Ecology : Faculty of Sciences University of Novi Sad., 43(1), 112.
https://hdl.handle.net/21.15107/rcub_vinar_11017

Grbić M, Gemović B, Davidović R, Kartelj A, Matić D. Prediction of GO terms for IDPs based on highly connected components in PPI networks. in Biologia Serbica : Belgrade BioInformatics Conference : BelBi2021 : program and the book of abstracts; June 21-25. 2021;43(1):112.
https://hdl.handle.net/21.15107/rcub_vinar_11017 .

Grbić, Milana, Gemović, Branislava, Davidović, Radoslav, Kartelj, Aleksandar, Matić, Dragan, "Prediction of GO terms for IDPs based on highly connected components in PPI networks" in Biologia Serbica : Belgrade BioInformatics Conference : BelBi2021 : program and the book of abstracts; June 21-25, 43, no. 1 (2021):112,
https://hdl.handle.net/21.15107/rcub_vinar_11017 .

TY  - JOUR
AU  - Davidović, Radoslav S.
AU  - Perović, Vladimir R.
AU  - Gemović, Branislava S.
AU  - Veljković, Nevena V.
PY  - 2020
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/9666
AB  - Although various tools for Gene Ontology (GO) term enrichment analysis are available, there is still room for improvement. Hence, we present DiNGO, a standalone application based on an open source code from BiNGO, a widely-used application to assess the overrepresentation of GO categories. Besides facilitating GO term enrichment analyses, DiNGO has been developed to allow for convenient Human Phenotype Ontology (HPO) term overrepresentation investigation. This is an important contribution considering the increasing interest in HPO in scientific research and its potential in clinical settings. DiNGO supports gene/protein identifier conversion and an automatic updating of GO and HPO annotation resources. Finally, DiNGO can rapidly process a large amount of data due to its multithread design.DiNGO is implemented in the JAVA language, and its source code, example datasets and instructions are available on GitHub: https://github.com/radoslav180/DiNGO. A pre-compiled jar file is available at: https://www.vin.bg.ac.rs/180/tools/DiNGO.php.Supplementary data are available at Bioinformatics online.
T2  - Bioinformatics
T1  - DiNGO: standalone application for Gene Ontology and Human Phenotype Ontology term enrichment analysis
VL  - 36
IS  - 6
SP  - 1981
EP  - 1982
DO  - 10.1093/bioinformatics/btz836
ER  - 

@article{
author = "Davidović, Radoslav S. and Perović, Vladimir R. and Gemović, Branislava S. and Veljković, Nevena V.",
year = "2020",
abstract = "Although various tools for Gene Ontology (GO) term enrichment analysis are available, there is still room for improvement. Hence, we present DiNGO, a standalone application based on an open source code from BiNGO, a widely-used application to assess the overrepresentation of GO categories. Besides facilitating GO term enrichment analyses, DiNGO has been developed to allow for convenient Human Phenotype Ontology (HPO) term overrepresentation investigation. This is an important contribution considering the increasing interest in HPO in scientific research and its potential in clinical settings. DiNGO supports gene/protein identifier conversion and an automatic updating of GO and HPO annotation resources. Finally, DiNGO can rapidly process a large amount of data due to its multithread design.DiNGO is implemented in the JAVA language, and its source code, example datasets and instructions are available on GitHub: https://github.com/radoslav180/DiNGO. A pre-compiled jar file is available at: https://www.vin.bg.ac.rs/180/tools/DiNGO.php.Supplementary data are available at Bioinformatics online.",
journal = "Bioinformatics",
title = "DiNGO: standalone application for Gene Ontology and Human Phenotype Ontology term enrichment analysis",
volume = "36",
number = "6",
pages = "1981-1982",
doi = "10.1093/bioinformatics/btz836"
}

Davidović, R. S., Perović, V. R., Gemović, B. S.,& Veljković, N. V.. (2020). DiNGO: standalone application for Gene Ontology and Human Phenotype Ontology term enrichment analysis. in Bioinformatics, 36(6), 1981-1982.
https://doi.org/10.1093/bioinformatics/btz836

Davidović RS, Perović VR, Gemović BS, Veljković NV. DiNGO: standalone application for Gene Ontology and Human Phenotype Ontology term enrichment analysis. in Bioinformatics. 2020;36(6):1981-1982.
doi:10.1093/bioinformatics/btz836 .

Davidović, Radoslav S., Perović, Vladimir R., Gemović, Branislava S., Veljković, Nevena V., "DiNGO: standalone application for Gene Ontology and Human Phenotype Ontology term enrichment analysis" in Bioinformatics, 36, no. 6 (2020):1981-1982,
https://doi.org/10.1093/bioinformatics/btz836 . .

TY  - JOUR
AU  - Gemović, Branislava S.
AU  - Šumonja, Neven
AU  - Davidović, Radoslav
AU  - Perović, Vladimir
AU  - Veljković, Nevena V.
PY  - 2019
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/10775
AB  - Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology.

Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions.

Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions.

Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs.

Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.
T2  - Current Medicinal Chemistry
T1  - Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes
VL  - 26
IS  - 21
SP  - 3890
EP  - 3910
DO  - 10.2174/0929867325666180214113704
ER  - 

@article{
author = "Gemović, Branislava S. and Šumonja, Neven and Davidović, Radoslav and Perović, Vladimir and Veljković, Nevena V.",
year = "2019",
abstract = "Background: The significant number of protein-protein interactions (PPIs) discovered by harnessing concomitant advances in the fields of sequencing, crystallography, spectrometry and two-hybrid screening suggests astonishing prospects for remodelling drug discovery. The PPI space which includes up to 650 000 entities is a remarkable reservoir of potential therapeutic targets for every human disease. In order to allow modern drug discovery programs to leverage this, we should be able to discern complete PPI maps associated with a specific disorder and corresponding normal physiology.

Objective: Here, we will review community available computational programs for predicting PPIs and web-based resources for storing experimentally annotated interactions.

Methods: We compared the capacities of prediction tools: iLoops, Struck2Net, HOMCOS, COTH, PrePPI, InterPreTS and PRISM to predict recently discovered protein interactions.

Results: We described sequence-based and structure-based PPI prediction tools and addressed their peculiarities. Additionally, since the usefulness of prediction algorithms critically depends on the quality and quantity of the experimental data they are built on; we extensively discussed community resources for protein interactions. We focused on the active and recently updated primary and secondary PPI databases, repositories specialized to the subject or species, as well as databases that include both experimental and predicted PPIs.

Conclusion: PPI complexes are the basis of important physiological processes and therefore, possible targets for cell-penetrating ligands. Reliable computational PPI predictions can speed up new target discoveries through prioritization of therapeutically relevant protein–protein complexes for experimental studies.",
journal = "Current Medicinal Chemistry",
title = "Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes",
volume = "26",
number = "21",
pages = "3890-3910",
doi = "10.2174/0929867325666180214113704"
}

Gemović, B. S., Šumonja, N., Davidović, R., Perović, V.,& Veljković, N. V.. (2019). Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes. in Current Medicinal Chemistry, 26(21), 3890-3910.
https://doi.org/10.2174/0929867325666180214113704

Gemović BS, Šumonja N, Davidović R, Perović V, Veljković NV. Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes. in Current Medicinal Chemistry. 2019;26(21):3890-3910.
doi:10.2174/0929867325666180214113704 .

Gemović, Branislava S., Šumonja, Neven, Davidović, Radoslav, Perović, Vladimir, Veljković, Nevena V., "Mapping of Protein-Protein Interactions: Web-Based Resources for Revealing Interactomes" in Current Medicinal Chemistry, 26, no. 21 (2019):3890-3910,
https://doi.org/10.2174/0929867325666180214113704 . .

TY  - JOUR
AU  - Šumonja, Neven
AU  - Gemović, Branislava S.
AU  - Veljković, Nevena V.
AU  - Perović, Vladimir R.
PY  - 2019
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/8395
AB  - Over the last decade, various machine learning (ML) and statistical approaches for protein–protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understanding of life. Efficient ML approaches require informative and non-redundant features. In this paper, we introduce novel types of expert-crafted sequence, evolutionary and graph features and apply automatic feature engineering to further expand feature space to improve predictive modeling. The two-step automatic feature-engineering process encompasses the hybrid method for feature generation and unsupervised feature selection, followed by supervised feature selection through a genetic algorithm (GA). The optimization of both steps allows the feature-engineering procedure to operate on a large transformed feature space with no considerable computational cost and to efficiently provide newly engineered features. Based on GA and correlation filtering, we developed a stacking algorithm GA-STACK for automatic ensembling of different ML algorithms to improve prediction performance. We introduced a unified method, HP-GAS, for the prediction of human PPIs, which incorporates GA-STACK and rests on both expert-crafted and 40% of newly engineered features. The extensive cross validation and comparison with the state-of-the-art methods showed that HP-GAS represents currently the most efficient method for proteome-wide forecasting of protein interactions, with prediction efficacy of 0.93 AUC and 0.85 accuracy. We implemented the HP-GAS method as a free standalone application which is a time-efficient and easy-to-use tool. HP-GAS software with supplementary data can be downloaded from: http://www.vinca.rs/180/tools/HP-GAS.php. © 2019, Springer-Verlag GmbH Austria, part of Springer Nature.
T2  - Amino Acids
T1  - Automated feature engineering improves prediction of protein–protein interactions
VL  - 51
IS  - 8
SP  - 1187
EP  - 1200
DO  - 10.1007/s00726-019-02756-9
ER  - 

@article{
author = "Šumonja, Neven and Gemović, Branislava S. and Veljković, Nevena V. and Perović, Vladimir R.",
year = "2019",
abstract = "Over the last decade, various machine learning (ML) and statistical approaches for protein–protein interaction (PPI) predictions have been developed to help annotating functional interactions among proteins, essential for our system-level understanding of life. Efficient ML approaches require informative and non-redundant features. In this paper, we introduce novel types of expert-crafted sequence, evolutionary and graph features and apply automatic feature engineering to further expand feature space to improve predictive modeling. The two-step automatic feature-engineering process encompasses the hybrid method for feature generation and unsupervised feature selection, followed by supervised feature selection through a genetic algorithm (GA). The optimization of both steps allows the feature-engineering procedure to operate on a large transformed feature space with no considerable computational cost and to efficiently provide newly engineered features. Based on GA and correlation filtering, we developed a stacking algorithm GA-STACK for automatic ensembling of different ML algorithms to improve prediction performance. We introduced a unified method, HP-GAS, for the prediction of human PPIs, which incorporates GA-STACK and rests on both expert-crafted and 40% of newly engineered features. The extensive cross validation and comparison with the state-of-the-art methods showed that HP-GAS represents currently the most efficient method for proteome-wide forecasting of protein interactions, with prediction efficacy of 0.93 AUC and 0.85 accuracy. We implemented the HP-GAS method as a free standalone application which is a time-efficient and easy-to-use tool. HP-GAS software with supplementary data can be downloaded from: http://www.vinca.rs/180/tools/HP-GAS.php. © 2019, Springer-Verlag GmbH Austria, part of Springer Nature.",
journal = "Amino Acids",
title = "Automated feature engineering improves prediction of protein–protein interactions",
volume = "51",
number = "8",
pages = "1187-1200",
doi = "10.1007/s00726-019-02756-9"
}

Šumonja, N., Gemović, B. S., Veljković, N. V.,& Perović, V. R.. (2019). Automated feature engineering improves prediction of protein–protein interactions. in Amino Acids, 51(8), 1187-1200.
https://doi.org/10.1007/s00726-019-02756-9

Šumonja N, Gemović BS, Veljković NV, Perović VR. Automated feature engineering improves prediction of protein–protein interactions. in Amino Acids. 2019;51(8):1187-1200.
doi:10.1007/s00726-019-02756-9 .

Šumonja, Neven, Gemović, Branislava S., Veljković, Nevena V., Perović, Vladimir R., "Automated feature engineering improves prediction of protein–protein interactions" in Amino Acids, 51, no. 8 (2019):1187-1200,
https://doi.org/10.1007/s00726-019-02756-9 . .

TY  - JOUR
AU  - Zhou, Naihui
AU  - Jiang, Yuxiang
AU  - Bergquist, Timothy R
AU  - Lee, Alexandra J
AU  - Kacsoh, Balint Z
AU  - Crocker, Alex W
AU  - Lewis, Kimberley A
AU  - Georghiou, George
AU  - Nguyen, Huy N
AU  - Hamid, Md Nafiz
AU  - Davis, Larry
AU  - Dogan, Tunca
AU  - Atalay, Volkan
AU  - Rifaioglu, Ahmet S
AU  - Dalkıran, Alperen
AU  - Cetin Atalay, Rengul
AU  - Zhang, Chengxin
AU  - Hurto, Rebecca L
AU  - Freddolino, Peter L
AU  - Zhang, Yang
AU  - Bhat, Prajwal
AU  - Supek, Fran
AU  - Fernández, José M
AU  - Gemović, Branislava S.
AU  - Perović, Vladimir R.
AU  - Davidović, Radoslav S.
AU  - Šumonja, Neven
AU  - Veljković, Nevena V.
AU  - Asgari, Ehsaneddin
AU  - Mofrad, Mohammad R.K.
AU  - Profiti, Giuseppe
AU  - Savojardo, Castrense
AU  - Martelli, Pier Luigi
AU  - Casadio, Rita
AU  - Boecker, Florian
AU  - Schoof, Heiko
AU  - Kahanda, Indika
AU  - Thurlby, Natalie
AU  - McHardy, Alice C
AU  - Renaux, Alexandre
AU  - Saidi, Rabie
AU  - Gough, Julian
AU  - Freitas, Alex A
AU  - Antczak, Magdalena
AU  - Fabris, Fabio
AU  - Wass, Mark N
AU  - Hou, Jie
AU  - Cheng, Jianlin
AU  - Wang, Zheng
AU  - Romero, Alfonso E
AU  - Paccanaro, Alberto
AU  - Yang, Haixuan
AU  - Goldberg, Tatyana
AU  - Zhao, Chenguang
AU  - Holm, Liisa
AU  - Törönen, Petri
AU  - Medlar, Alan J
AU  - Zosa, Elaine
AU  - Borukhov, Itamar
AU  - Novikov, Ilya
AU  - Wilkins, Angela
AU  - Lichtarge, Olivier
AU  - Chi, Po-Han
AU  - Tseng, Wei-Cheng
AU  - Linial, Michal
AU  - Rose, Peter W
AU  - Dessimoz, Christophe
AU  - Vidulin, Vedrana
AU  - Dzeroski, Saso
AU  - Sillitoe, Ian
AU  - Das, Sayoni
AU  - Lees, Jonathan Gill
AU  - Jones, David T
AU  - Wan, Cen
AU  - Cozzetto, Domenico
AU  - Fa, Rui
AU  - Torres, Mateo
AU  - Warwick Vesztrocy, Alex
AU  - Rodriguez, Jose Manuel
AU  - Tress, Michael L
AU  - Frasca, Marco
AU  - Notaro, Marco
AU  - Grossi, Giuliano
AU  - Petrini, Alessandro
AU  - Re, Matteo
AU  - Valentini, Giorgio
AU  - Mesiti, Marco
AU  - Roche, Daniel B
AU  - Reeb, Jonas
AU  - Ritchie, David W
AU  - Aridhi, Sabeur
AU  - Alborzi, Seyed Ziaeddin
AU  - Devignes, Marie-Dominique
AU  - Koo, Da Chen Emily
AU  - Bonneau, Richard
AU  - Gligorijević, Vladimir
AU  - Barot, Meet
AU  - Fang, Hai
AU  - Toppo, Stefano
AU  - Lavezzo, Enrico
AU  - Falda, Marco
AU  - Berselli, Michele
AU  - Tosatto, Silvio C.E.
AU  - Carraro, Marco
AU  - Piovesan, Damiano
AU  - Ur Rehman, Hafeez
AU  - Mao, Qizhong
AU  - Zhang, Shanshan
AU  - Vucetic, Slobodan
AU  - Black, Gage S
AU  - Jo, Dane
AU  - Suh, Erica
AU  - Dayton, Jonathan B
AU  - Larsen, Dallas J
AU  - Omdahl, Ashton R
AU  - McGuffin, Liam J
AU  - Brackenridge, Danielle A
AU  - Babbitt, Patricia C
AU  - Yunes, Jeffrey M
AU  - Fontana, Paolo
AU  - Zhang, Feng
AU  - Zhu, Shanfeng
AU  - You, Ronghui
AU  - Zhang, Zihan
AU  - Dai, Suyang
AU  - Yao, Shuwei
AU  - Tian, Weidong
AU  - Cao, Renzhi
AU  - Chandler, Caleb
AU  - Amezola, Miguel
AU  - Johnson, Devon
AU  - Chang, Jia-Ming
AU  - Liao, Wen-Hung
AU  - Liu, Yi-Wei
AU  - Pascarelli, Stefano
AU  - Frank, Yotam
AU  - Hoehndorf, Robert
AU  - Kulmanov, Maxat
AU  - Boudellioua, Imane
AU  - Politano, Gianfranco
AU  - Di Carlo, Stefano
AU  - Benso, Alfredo
AU  - Hakala, Kai
AU  - Ginter, Filip
AU  - Mehryary, Farrokh
AU  - Kaewphan, Suwisa
AU  - Björne, Jari
AU  - Moen, Hans
AU  - Tolvanen, Martti E.E.
AU  - Salakoski, Tapio
AU  - Kihara, Daisuke
AU  - Jain, Aashish
AU  - Šmuc, Tomislav
AU  - Altenhoff, Adrian
AU  - Ben-Hur, Asa
AU  - Rost, Burkhard
AU  - Brenner, Steven E
AU  - Orengo, Christine A
AU  - Jeffery, Constance J
AU  - Bosco, Giovanni
AU  - Hogan, Deborah A
AU  - Martin, Maria J
AU  - O’Donovan, Claire
AU  - Mooney, Sean D
AU  - Greene, Casey S
AU  - Radivojac, Predrag
AU  - Friedberg, Iddo
PY  - 2019
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/8655
AB  - Background: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-Term memory. Conclusion: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens. © 2019 The Author(s).
T2  - Genome Biology
T1  - The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens
VL  - 20
IS  - 1
SP  - 244
DO  - 10.1186/s13059-019-1835-8
ER  - 

@article{
author = "Zhou, Naihui and Jiang, Yuxiang and Bergquist, Timothy R and Lee, Alexandra J and Kacsoh, Balint Z and Crocker, Alex W and Lewis, Kimberley A and Georghiou, George and Nguyen, Huy N and Hamid, Md Nafiz and Davis, Larry and Dogan, Tunca and Atalay, Volkan and Rifaioglu, Ahmet S and Dalkıran, Alperen and Cetin Atalay, Rengul and Zhang, Chengxin and Hurto, Rebecca L and Freddolino, Peter L and Zhang, Yang and Bhat, Prajwal and Supek, Fran and Fernández, José M and Gemović, Branislava S. and Perović, Vladimir R. and Davidović, Radoslav S. and Šumonja, Neven and Veljković, Nevena V. and Asgari, Ehsaneddin and Mofrad, Mohammad R.K. and Profiti, Giuseppe and Savojardo, Castrense and Martelli, Pier Luigi and Casadio, Rita and Boecker, Florian and Schoof, Heiko and Kahanda, Indika and Thurlby, Natalie and McHardy, Alice C and Renaux, Alexandre and Saidi, Rabie and Gough, Julian and Freitas, Alex A and Antczak, Magdalena and Fabris, Fabio and Wass, Mark N and Hou, Jie and Cheng, Jianlin and Wang, Zheng and Romero, Alfonso E and Paccanaro, Alberto and Yang, Haixuan and Goldberg, Tatyana and Zhao, Chenguang and Holm, Liisa and Törönen, Petri and Medlar, Alan J and Zosa, Elaine and Borukhov, Itamar and Novikov, Ilya and Wilkins, Angela and Lichtarge, Olivier and Chi, Po-Han and Tseng, Wei-Cheng and Linial, Michal and Rose, Peter W and Dessimoz, Christophe and Vidulin, Vedrana and Dzeroski, Saso and Sillitoe, Ian and Das, Sayoni and Lees, Jonathan Gill and Jones, David T and Wan, Cen and Cozzetto, Domenico and Fa, Rui and Torres, Mateo and Warwick Vesztrocy, Alex and Rodriguez, Jose Manuel and Tress, Michael L and Frasca, Marco and Notaro, Marco and Grossi, Giuliano and Petrini, Alessandro and Re, Matteo and Valentini, Giorgio and Mesiti, Marco and Roche, Daniel B and Reeb, Jonas and Ritchie, David W and Aridhi, Sabeur and Alborzi, Seyed Ziaeddin and Devignes, Marie-Dominique and Koo, Da Chen Emily and Bonneau, Richard and Gligorijević, Vladimir and Barot, Meet and Fang, Hai and Toppo, Stefano and Lavezzo, Enrico and Falda, Marco and Berselli, Michele and Tosatto, Silvio C.E. and Carraro, Marco and Piovesan, Damiano and Ur Rehman, Hafeez and Mao, Qizhong and Zhang, Shanshan and Vucetic, Slobodan and Black, Gage S and Jo, Dane and Suh, Erica and Dayton, Jonathan B and Larsen, Dallas J and Omdahl, Ashton R and McGuffin, Liam J and Brackenridge, Danielle A and Babbitt, Patricia C and Yunes, Jeffrey M and Fontana, Paolo and Zhang, Feng and Zhu, Shanfeng and You, Ronghui and Zhang, Zihan and Dai, Suyang and Yao, Shuwei and Tian, Weidong and Cao, Renzhi and Chandler, Caleb and Amezola, Miguel and Johnson, Devon and Chang, Jia-Ming and Liao, Wen-Hung and Liu, Yi-Wei and Pascarelli, Stefano and Frank, Yotam and Hoehndorf, Robert and Kulmanov, Maxat and Boudellioua, Imane and Politano, Gianfranco and Di Carlo, Stefano and Benso, Alfredo and Hakala, Kai and Ginter, Filip and Mehryary, Farrokh and Kaewphan, Suwisa and Björne, Jari and Moen, Hans and Tolvanen, Martti E.E. and Salakoski, Tapio and Kihara, Daisuke and Jain, Aashish and Šmuc, Tomislav and Altenhoff, Adrian and Ben-Hur, Asa and Rost, Burkhard and Brenner, Steven E and Orengo, Christine A and Jeffery, Constance J and Bosco, Giovanni and Hogan, Deborah A and Martin, Maria J and O’Donovan, Claire and Mooney, Sean D and Greene, Casey S and Radivojac, Predrag and Friedberg, Iddo",
year = "2019",
abstract = "Background: The Critical Assessment of Functional Annotation (CAFA) is an ongoing, global, community-driven effort to evaluate and improve the computational annotation of protein function. Results: Here, we report on the results of the third CAFA challenge, CAFA3, that featured an expanded analysis over the previous CAFA rounds, both in terms of volume of data analyzed and the types of analysis performed. In a novel and major new development, computational predictions and assessment goals drove some of the experimental assays, resulting in new functional annotations for more than 1000 genes. Specifically, we performed experimental whole-genome mutation screening in Candida albicans and Pseudomonas aureginosa genomes, which provided us with genome-wide experimental data for genes associated with biofilm formation and motility. We further performed targeted assays on selected genes in Drosophila melanogaster, which we suspected of being involved in long-Term memory. Conclusion: We conclude that while predictions of the molecular function and biological process annotations have slightly improved over time, those of the cellular component have not. Term-centric prediction of experimental annotations remains equally challenging; although the performance of the top methods is significantly better than the expectations set by baseline methods in C. albicans and D. melanogaster, it leaves considerable room and need for improvement. Finally, we report that the CAFA community now involves a broad range of participants with expertise in bioinformatics, biological experimentation, biocuration, and bio-ontologies, working together to improve functional annotation, computational function prediction, and our ability to manage big data in the era of large experimental screens. © 2019 The Author(s).",
journal = "Genome Biology",
title = "The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens",
volume = "20",
number = "1",
pages = "244",
doi = "10.1186/s13059-019-1835-8"
}

Zhou, N., Jiang, Y., Bergquist, T. R., Lee, A. J., Kacsoh, B. Z., Crocker, A. W., Lewis, K. A., Georghiou, G., Nguyen, H. N., Hamid, M. N., Davis, L., Dogan, T., Atalay, V., Rifaioglu, A. S., Dalkıran, A., Cetin Atalay, R., Zhang, C., Hurto, R. L., Freddolino, P. L., Zhang, Y., Bhat, P., Supek, F., Fernández, J. M., Gemović, B. S., Perović, V. R., Davidović, R. S., Šumonja, N., Veljković, N. V., Asgari, E., Mofrad, M. R.K., Profiti, G., Savojardo, C., Martelli, P. L., Casadio, R., Boecker, F., Schoof, H., Kahanda, I., Thurlby, N., McHardy, A. C., Renaux, A., Saidi, R., Gough, J., Freitas, A. A., Antczak, M., Fabris, F., Wass, M. N., Hou, J., Cheng, J., Wang, Z., Romero, A. E., Paccanaro, A., Yang, H., Goldberg, T., Zhao, C., Holm, L., Törönen, P., Medlar, A. J., Zosa, E., Borukhov, I., Novikov, I., Wilkins, A., Lichtarge, O., Chi, P., Tseng, W., Linial, M., Rose, P. W., Dessimoz, C., Vidulin, V., Dzeroski, S., Sillitoe, I., Das, S., Lees, J. G., Jones, D. T., Wan, C., Cozzetto, D., Fa, R., Torres, M., Warwick Vesztrocy, A., Rodriguez, J. M., Tress, M. L., Frasca, M., Notaro, M., Grossi, G., Petrini, A., Re, M., Valentini, G., Mesiti, M., Roche, D. B., Reeb, J., Ritchie, D. W., Aridhi, S., Alborzi, S. Z., Devignes, M., Koo, D. C. E., Bonneau, R., Gligorijević, V., Barot, M., Fang, H., Toppo, S., Lavezzo, E., Falda, M., Berselli, M., Tosatto, S. C.E., Carraro, M., Piovesan, D., Ur Rehman, H., Mao, Q., Zhang, S., Vucetic, S., Black, G. S., Jo, D., Suh, E., Dayton, J. B., Larsen, D. J., Omdahl, A. R., McGuffin, L. J., Brackenridge, D. A., Babbitt, P. C., Yunes, J. M., Fontana, P., Zhang, F., Zhu, S., You, R., Zhang, Z., Dai, S., Yao, S., Tian, W., Cao, R., Chandler, C., Amezola, M., Johnson, D., Chang, J., Liao, W., Liu, Y., Pascarelli, S., Frank, Y., Hoehndorf, R., Kulmanov, M., Boudellioua, I., Politano, G., Di Carlo, S., Benso, A., Hakala, K., Ginter, F., Mehryary, F., Kaewphan, S., Björne, J., Moen, H., Tolvanen, M. E.E., Salakoski, T., Kihara, D., Jain, A., Šmuc, T., Altenhoff, A., Ben-Hur, A., Rost, B., Brenner, S. E., Orengo, C. A., Jeffery, C. J., Bosco, G., Hogan, D. A., Martin, M. J., O’Donovan, C., Mooney, S. D., Greene, C. S., Radivojac, P.,& Friedberg, I.. (2019). The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. in Genome Biology, 20(1), 244.
https://doi.org/10.1186/s13059-019-1835-8

Zhou N, Jiang Y, Bergquist TR, Lee AJ, Kacsoh BZ, Crocker AW, Lewis KA, Georghiou G, Nguyen HN, Hamid MN, Davis L, Dogan T, Atalay V, Rifaioglu AS, Dalkıran A, Cetin Atalay R, Zhang C, Hurto RL, Freddolino PL, Zhang Y, Bhat P, Supek F, Fernández JM, Gemović BS, Perović VR, Davidović RS, Šumonja N, Veljković NV, Asgari E, Mofrad MR, Profiti G, Savojardo C, Martelli PL, Casadio R, Boecker F, Schoof H, Kahanda I, Thurlby N, McHardy AC, Renaux A, Saidi R, Gough J, Freitas AA, Antczak M, Fabris F, Wass MN, Hou J, Cheng J, Wang Z, Romero AE, Paccanaro A, Yang H, Goldberg T, Zhao C, Holm L, Törönen P, Medlar AJ, Zosa E, Borukhov I, Novikov I, Wilkins A, Lichtarge O, Chi P, Tseng W, Linial M, Rose PW, Dessimoz C, Vidulin V, Dzeroski S, Sillitoe I, Das S, Lees JG, Jones DT, Wan C, Cozzetto D, Fa R, Torres M, Warwick Vesztrocy A, Rodriguez JM, Tress ML, Frasca M, Notaro M, Grossi G, Petrini A, Re M, Valentini G, Mesiti M, Roche DB, Reeb J, Ritchie DW, Aridhi S, Alborzi SZ, Devignes M, Koo DCE, Bonneau R, Gligorijević V, Barot M, Fang H, Toppo S, Lavezzo E, Falda M, Berselli M, Tosatto SC, Carraro M, Piovesan D, Ur Rehman H, Mao Q, Zhang S, Vucetic S, Black GS, Jo D, Suh E, Dayton JB, Larsen DJ, Omdahl AR, McGuffin LJ, Brackenridge DA, Babbitt PC, Yunes JM, Fontana P, Zhang F, Zhu S, You R, Zhang Z, Dai S, Yao S, Tian W, Cao R, Chandler C, Amezola M, Johnson D, Chang J, Liao W, Liu Y, Pascarelli S, Frank Y, Hoehndorf R, Kulmanov M, Boudellioua I, Politano G, Di Carlo S, Benso A, Hakala K, Ginter F, Mehryary F, Kaewphan S, Björne J, Moen H, Tolvanen ME, Salakoski T, Kihara D, Jain A, Šmuc T, Altenhoff A, Ben-Hur A, Rost B, Brenner SE, Orengo CA, Jeffery CJ, Bosco G, Hogan DA, Martin MJ, O’Donovan C, Mooney SD, Greene CS, Radivojac P, Friedberg I. The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. in Genome Biology. 2019;20(1):244.
doi:10.1186/s13059-019-1835-8 .

Zhou, Naihui, Jiang, Yuxiang, Bergquist, Timothy R, Lee, Alexandra J, Kacsoh, Balint Z, Crocker, Alex W, Lewis, Kimberley A, Georghiou, George, Nguyen, Huy N, Hamid, Md Nafiz, Davis, Larry, Dogan, Tunca, Atalay, Volkan, Rifaioglu, Ahmet S, Dalkıran, Alperen, Cetin Atalay, Rengul, Zhang, Chengxin, Hurto, Rebecca L, Freddolino, Peter L, Zhang, Yang, Bhat, Prajwal, Supek, Fran, Fernández, José M, Gemović, Branislava S., Perović, Vladimir R., Davidović, Radoslav S., Šumonja, Neven, Veljković, Nevena V., Asgari, Ehsaneddin, Mofrad, Mohammad R.K., Profiti, Giuseppe, Savojardo, Castrense, Martelli, Pier Luigi, Casadio, Rita, Boecker, Florian, Schoof, Heiko, Kahanda, Indika, Thurlby, Natalie, McHardy, Alice C, Renaux, Alexandre, Saidi, Rabie, Gough, Julian, Freitas, Alex A, Antczak, Magdalena, Fabris, Fabio, Wass, Mark N, Hou, Jie, Cheng, Jianlin, Wang, Zheng, Romero, Alfonso E, Paccanaro, Alberto, Yang, Haixuan, Goldberg, Tatyana, Zhao, Chenguang, Holm, Liisa, Törönen, Petri, Medlar, Alan J, Zosa, Elaine, Borukhov, Itamar, Novikov, Ilya, Wilkins, Angela, Lichtarge, Olivier, Chi, Po-Han, Tseng, Wei-Cheng, Linial, Michal, Rose, Peter W, Dessimoz, Christophe, Vidulin, Vedrana, Dzeroski, Saso, Sillitoe, Ian, Das, Sayoni, Lees, Jonathan Gill, Jones, David T, Wan, Cen, Cozzetto, Domenico, Fa, Rui, Torres, Mateo, Warwick Vesztrocy, Alex, Rodriguez, Jose Manuel, Tress, Michael L, Frasca, Marco, Notaro, Marco, Grossi, Giuliano, Petrini, Alessandro, Re, Matteo, Valentini, Giorgio, Mesiti, Marco, Roche, Daniel B, Reeb, Jonas, Ritchie, David W, Aridhi, Sabeur, Alborzi, Seyed Ziaeddin, Devignes, Marie-Dominique, Koo, Da Chen Emily, Bonneau, Richard, Gligorijević, Vladimir, Barot, Meet, Fang, Hai, Toppo, Stefano, Lavezzo, Enrico, Falda, Marco, Berselli, Michele, Tosatto, Silvio C.E., Carraro, Marco, Piovesan, Damiano, Ur Rehman, Hafeez, Mao, Qizhong, Zhang, Shanshan, Vucetic, Slobodan, Black, Gage S, Jo, Dane, Suh, Erica, Dayton, Jonathan B, Larsen, Dallas J, Omdahl, Ashton R, McGuffin, Liam J, Brackenridge, Danielle A, Babbitt, Patricia C, Yunes, Jeffrey M, Fontana, Paolo, Zhang, Feng, Zhu, Shanfeng, You, Ronghui, Zhang, Zihan, Dai, Suyang, Yao, Shuwei, Tian, Weidong, Cao, Renzhi, Chandler, Caleb, Amezola, Miguel, Johnson, Devon, Chang, Jia-Ming, Liao, Wen-Hung, Liu, Yi-Wei, Pascarelli, Stefano, Frank, Yotam, Hoehndorf, Robert, Kulmanov, Maxat, Boudellioua, Imane, Politano, Gianfranco, Di Carlo, Stefano, Benso, Alfredo, Hakala, Kai, Ginter, Filip, Mehryary, Farrokh, Kaewphan, Suwisa, Björne, Jari, Moen, Hans, Tolvanen, Martti E.E., Salakoski, Tapio, Kihara, Daisuke, Jain, Aashish, Šmuc, Tomislav, Altenhoff, Adrian, Ben-Hur, Asa, Rost, Burkhard, Brenner, Steven E, Orengo, Christine A, Jeffery, Constance J, Bosco, Giovanni, Hogan, Deborah A, Martin, Maria J, O’Donovan, Claire, Mooney, Sean D, Greene, Casey S, Radivojac, Predrag, Friedberg, Iddo, "The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens" in Genome Biology, 20, no. 1 (2019):244,
https://doi.org/10.1186/s13059-019-1835-8 . .

TY  - CONF
AU  - Perović, Vladimir R.
AU  - Gemović, Branislava S.
AU  - Veljković, Veljko
AU  - Glišić, Sanja
AU  - Veljković, Nevena V.
PY  - 2017
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/10770
AB  - Summary. Coding genetic variants can have profound effects on protein function. Computational tools for the prediction of these effects are used to complement and guide experimental biological studies. Phylogenetic analyses that determine the evolutionary relationship among related sequences are commonly used to distinguish between functionally significant and insignificant gene variations. Here, we have reviewed applications of the non-alignment sequence analyses method for phylogenetic analyses, ISTREE. Furthermore, we assessed how an unsupervised ISTREE-d3 method based on the universal d3 measure responds to this task compared to supervised and semi-supervised ISTREE methods that were previously used in two studies. The findings presented here suggest that ISTREE-d3 can efficiently substitute for the corresponding supervised models, given that it is more suitable for automatic applications. In conclusion, the ISTREE-d3 method has a broad biological relevance and represents a promising approach in functional assessment of coding gene variations.
C3  - Biologica Serbica
T1  - Annotation Of The Functional Impact Of Coding Genetic Variants
VL  - 39
IS  - 1
SP  - 74
EP  - 82
DO  - 10.5281/zenodo.826908
ER  - 

@conference{
author = "Perović, Vladimir R. and Gemović, Branislava S. and Veljković, Veljko and Glišić, Sanja and Veljković, Nevena V.",
year = "2017",
abstract = "Summary. Coding genetic variants can have profound effects on protein function. Computational tools for the prediction of these effects are used to complement and guide experimental biological studies. Phylogenetic analyses that determine the evolutionary relationship among related sequences are commonly used to distinguish between functionally significant and insignificant gene variations. Here, we have reviewed applications of the non-alignment sequence analyses method for phylogenetic analyses, ISTREE. Furthermore, we assessed how an unsupervised ISTREE-d3 method based on the universal d3 measure responds to this task compared to supervised and semi-supervised ISTREE methods that were previously used in two studies. The findings presented here suggest that ISTREE-d3 can efficiently substitute for the corresponding supervised models, given that it is more suitable for automatic applications. In conclusion, the ISTREE-d3 method has a broad biological relevance and represents a promising approach in functional assessment of coding gene variations.",
journal = "Biologica Serbica",
title = "Annotation Of The Functional Impact Of Coding Genetic Variants",
volume = "39",
number = "1",
pages = "74-82",
doi = "10.5281/zenodo.826908"
}

Perović, V. R., Gemović, B. S., Veljković, V., Glišić, S.,& Veljković, N. V.. (2017). Annotation Of The Functional Impact Of Coding Genetic Variants. in Biologica Serbica, 39(1), 74-82.
https://doi.org/10.5281/zenodo.826908

Perović VR, Gemović BS, Veljković V, Glišić S, Veljković NV. Annotation Of The Functional Impact Of Coding Genetic Variants. in Biologica Serbica. 2017;39(1):74-82.
doi:10.5281/zenodo.826908 .

Perović, Vladimir R., Gemović, Branislava S., Veljković, Veljko, Glišić, Sanja, Veljković, Nevena V., "Annotation Of The Functional Impact Of Coding Genetic Variants" in Biologica Serbica, 39, no. 1 (2017):74-82,
https://doi.org/10.5281/zenodo.826908 . .

TY  - JOUR
AU  - Perović, Vladimir R.
AU  - Šumonja, Neven
AU  - Gemović, Branislava S.
AU  - Toska, Eneda
AU  - Roberts, Stefan G. E.
AU  - Veljković, Nevena V.
PY  - 2017
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/1468
AB  - The TRI_tool, a sequence-based web tool for prediction of protein interactions in the human transcriptional regulation, is intended for biomedical investigators who work on understanding the regulation of gene expression. It has an improved predictive performance due to the training on updated, human specific, experimentally validated datasets. The TRI_tool is designed to test up to 100 potential interactions with no time delay and to report both probabilities and binarized predictions.
T2  - Bioinformatics
T1  - TRI_tool: a web-tool for prediction of protein-protein interactions in human transcriptional regulation
VL  - 33
IS  - 2
SP  - 289
EP  - 291
DO  - 10.1093/bioinformatics/btw590
ER  - 

@article{
author = "Perović, Vladimir R. and Šumonja, Neven and Gemović, Branislava S. and Toska, Eneda and Roberts, Stefan G. E. and Veljković, Nevena V.",
year = "2017",
abstract = "The TRI_tool, a sequence-based web tool for prediction of protein interactions in the human transcriptional regulation, is intended for biomedical investigators who work on understanding the regulation of gene expression. It has an improved predictive performance due to the training on updated, human specific, experimentally validated datasets. The TRI_tool is designed to test up to 100 potential interactions with no time delay and to report both probabilities and binarized predictions.",
journal = "Bioinformatics",
title = "TRI_tool: a web-tool for prediction of protein-protein interactions in human transcriptional regulation",
volume = "33",
number = "2",
pages = "289-291",
doi = "10.1093/bioinformatics/btw590"
}

Perović, V. R., Šumonja, N., Gemović, B. S., Toska, E., Roberts, S. G. E.,& Veljković, N. V.. (2017). TRI_tool: a web-tool for prediction of protein-protein interactions in human transcriptional regulation. in Bioinformatics, 33(2), 289-291.
https://doi.org/10.1093/bioinformatics/btw590

Perović VR, Šumonja N, Gemović BS, Toska E, Roberts SGE, Veljković NV. TRI_tool: a web-tool for prediction of protein-protein interactions in human transcriptional regulation. in Bioinformatics. 2017;33(2):289-291.
doi:10.1093/bioinformatics/btw590 .

Perović, Vladimir R., Šumonja, Neven, Gemović, Branislava S., Toska, Eneda, Roberts, Stefan G. E., Veljković, Nevena V., "TRI_tool: a web-tool for prediction of protein-protein interactions in human transcriptional regulation" in Bioinformatics, 33, no. 2 (2017):289-291,
https://doi.org/10.1093/bioinformatics/btw590 . .

TY  - JOUR
AU  - Srdić-Rajić, Tatjana
AU  - Nikolić, Katarina M.
AU  - Čavić, Milena R.
AU  - Đokić, Ivana
AU  - Gemović, Branislava S.
AU  - Perović, Vladimir R.
AU  - Veljković, Nevena V.
PY  - 2016
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/884
AB  - Imidazoline I1 receptor signaling is associated with pathways that regulate cell viability leading to varied cell-type specific phenotypes. We demonstrated that the antihypertensive drug rilmenidine, a selective imidazoline I1 receptor agonist, modulates proliferation and stimulates the proapoptotic protein Bax thus inducing the perturbation of the mitochondrial pathway and apoptosis in human leukemic K562 cells. Rilmenidine acts through a mechanism which involves deactivation of Ras/MAP kinases ERK, p38 and JNK. Moreover, rilmenidine renders K562 cells, which are particularly resistant to chemotherapeutic agents, susceptible to the DNA damaging drug doxorubicin. The rilmenidine co-treatment with doxorubicin reverses G2/M arrest and triggers apoptotic response to DNA damage. Our data offer new insights into the pathways associated with imidazoline I1 receptor activation in K562 cells suggesting rilmenidine as a valuable tool to deepen our understanding of imidazoline I1 receptor signaling in hematologic malignancies and to search for medicinally active agents. (C) 2015 Elsevier B.V. All rights reserved.
PB  - Elsevier
T2  - European Journal of Pharmaceutical Sciences
T1  - Rilmenidine suppresses proliferation and promotes apoptosis via the mitochondrial pathway in human leukemic K562 cells
VL  - 81
SP  - 172
EP  - 180
DO  - 10.1016/j.ejps.2015.10.017
ER  - 

@article{
author = "Srdić-Rajić, Tatjana and Nikolić, Katarina M. and Čavić, Milena R. and Đokić, Ivana and Gemović, Branislava S. and Perović, Vladimir R. and Veljković, Nevena V.",
year = "2016",
abstract = "Imidazoline I1 receptor signaling is associated with pathways that regulate cell viability leading to varied cell-type specific phenotypes. We demonstrated that the antihypertensive drug rilmenidine, a selective imidazoline I1 receptor agonist, modulates proliferation and stimulates the proapoptotic protein Bax thus inducing the perturbation of the mitochondrial pathway and apoptosis in human leukemic K562 cells. Rilmenidine acts through a mechanism which involves deactivation of Ras/MAP kinases ERK, p38 and JNK. Moreover, rilmenidine renders K562 cells, which are particularly resistant to chemotherapeutic agents, susceptible to the DNA damaging drug doxorubicin. The rilmenidine co-treatment with doxorubicin reverses G2/M arrest and triggers apoptotic response to DNA damage. Our data offer new insights into the pathways associated with imidazoline I1 receptor activation in K562 cells suggesting rilmenidine as a valuable tool to deepen our understanding of imidazoline I1 receptor signaling in hematologic malignancies and to search for medicinally active agents. (C) 2015 Elsevier B.V. All rights reserved.",
publisher = "Elsevier",
journal = "European Journal of Pharmaceutical Sciences",
title = "Rilmenidine suppresses proliferation and promotes apoptosis via the mitochondrial pathway in human leukemic K562 cells",
volume = "81",
pages = "172-180",
doi = "10.1016/j.ejps.2015.10.017"
}

Srdić-Rajić, T., Nikolić, K. M., Čavić, M. R., Đokić, I., Gemović, B. S., Perović, V. R.,& Veljković, N. V.. (2016). Rilmenidine suppresses proliferation and promotes apoptosis via the mitochondrial pathway in human leukemic K562 cells. in European Journal of Pharmaceutical Sciences
Elsevier., 81, 172-180.
https://doi.org/10.1016/j.ejps.2015.10.017

Srdić-Rajić T, Nikolić KM, Čavić MR, Đokić I, Gemović BS, Perović VR, Veljković NV. Rilmenidine suppresses proliferation and promotes apoptosis via the mitochondrial pathway in human leukemic K562 cells. in European Journal of Pharmaceutical Sciences. 2016;81:172-180.
doi:10.1016/j.ejps.2015.10.017 .

Srdić-Rajić, Tatjana, Nikolić, Katarina M., Čavić, Milena R., Đokić, Ivana, Gemović, Branislava S., Perović, Vladimir R., Veljković, Nevena V., "Rilmenidine suppresses proliferation and promotes apoptosis via the mitochondrial pathway in human leukemic K562 cells" in European Journal of Pharmaceutical Sciences, 81 (2016):172-180,
https://doi.org/10.1016/j.ejps.2015.10.017 . .

TY  - JOUR
AU  - Jiang, Yuxiang
AU  - Gemović, Branislava S.
AU  - Glišić, Sanja
AU  - Perović, Vladimir R.
AU  - Veljković, Veljko
AU  - Veljković, Nevena V.
PY  - 2016
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/1244
AB  - Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.
T2  - Genome Biology
T1  - An expanded evaluation of protein function prediction methods shows an improvement in accuracy
VL  - 17
DO  - 10.1186/s13059-016-1037-6
ER  - 

@article{
author = "Jiang, Yuxiang and Gemović, Branislava S. and Glišić, Sanja and Perović, Vladimir R. and Veljković, Veljko and Veljković, Nevena V.",
year = "2016",
abstract = "Background: A major bottleneck in our understanding of the molecular underpinnings of life is the assignment of function to proteins. While molecular experiments provide the most reliable annotation of proteins, their relatively low throughput and restricted purview have led to an increasing role for computational function prediction. However, assessing methods for protein function prediction and tracking progress in the field remain challenging. Results: We conducted the second critical assessment of functional annotation (CAFA), a timed challenge to assess computational methods that automatically assign protein function. We evaluated 126 methods from 56 research groups for their ability to predict biological functions using Gene Ontology and gene-disease associations using Human Phenotype Ontology on a set of 3681 proteins from 18 species. CAFA2 featured expanded analysis compared with CAFA1, with regards to data set size, variety, and assessment metrics. To review progress in the field, the analysis compared the best methods from CAFA1 to those of CAFA2. Conclusions: The top-performing methods in CAFA2 outperformed those from CAFA1. This increased accuracy can be attributed to a combination of the growing number of experimental annotations and improved methods for function prediction. The assessment also revealed that the definition of top-performing algorithms is ontology specific, that different performance metrics can be used to probe the nature of accurate predictions, and the relative diversity of predictions in the biological process and human phenotype ontologies. While there was methodological improvement between CAFA1 and CAFA2, the interpretation of results and usefulness of individual methods remain context-dependent.",
journal = "Genome Biology",
title = "An expanded evaluation of protein function prediction methods shows an improvement in accuracy",
volume = "17",
doi = "10.1186/s13059-016-1037-6"
}

Jiang, Y., Gemović, B. S., Glišić, S., Perović, V. R., Veljković, V.,& Veljković, N. V.. (2016). An expanded evaluation of protein function prediction methods shows an improvement in accuracy. in Genome Biology, 17.
https://doi.org/10.1186/s13059-016-1037-6

Jiang Y, Gemović BS, Glišić S, Perović VR, Veljković V, Veljković NV. An expanded evaluation of protein function prediction methods shows an improvement in accuracy. in Genome Biology. 2016;17.
doi:10.1186/s13059-016-1037-6 .

Jiang, Yuxiang, Gemović, Branislava S., Glišić, Sanja, Perović, Vladimir R., Veljković, Veljko, Veljković, Nevena V., "An expanded evaluation of protein function prediction methods shows an improvement in accuracy" in Genome Biology, 17 (2016),
https://doi.org/10.1186/s13059-016-1037-6 . .

TY  - THES
AU  - Gemović, Branislava S.
PY  - 2015
UR  - http://eteze.bg.ac.rs/application/showtheses?thesesId=2622
UR  - https://fedorabg.bg.ac.rs/fedora/get/o:10712/bdef:Content/download
UR  - http://vbs.rs/scripts/cobiss?command=DISPLAY&base=70036&RID=1024898994
UR  - http://nardus.mpn.gov.rs/123456789/4929
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/7276
AB  - Mijeloidni maligniteti su klonalne bolesti ćelija mijeloidne krvne loze, koje su, pre svega, posledica poremećaja samoobnavljanja i diferencijacije hematopoetskih matičnih ćelija. Godišnja incidenca u Evropi je 7,5-8,6/100 000, a prosečna petogodišnja stopa preživljavanja pacijenata, uz primenu standardne terapije, je 37%. S obzirom da maligniteti najčešće nose više od jedne vodeće – ‘driver’ mutacije, pri čemu je velika intra- i interkancerska genetička heterogenost, postoji potreba za individualizovanim terapeutskim pristupom svakom pacijentu. Personalizovana medicina označava princip postizanja maksimalne terapijske efikasnosti kroz upotrebu ciljanih terapeutika kod biološki okarakterisanih pacijenata. Bioinformatika ima značajnu ulogu u identifikaciji ciljnih molekula za razvoj terapije, racionalnom dizajnu lekova i identifikaciji informativnih biomarkera za prateću dijagnostiku.Predmet istraživanja u ovom radu bili su geni i njihovi proteinski produkti koji imaju važnu ulogu u patogenezi mijeloidnih maligniteta. WT1 je transkripcioni faktor, koji kontroliše ekspresiju gena uključenih u apoptozu, proliferaciju i diferencijaciju. Ovaj gen ima povećanu ekspresiju u 70-90%, a mutiran je u ~10% akutnih mijeloidnih leukemija (AML) i učestvuje u preko 40 protein-protein interakcija, koje su vremenski i kontekstno zavisne. Mapiranje celokupne mreže WT1 kofaktora doprineće identifikaciji novih molekula i interakcija, koje su potencijalni targeti za ciljanu terapiju AML.Nedavno otkriven tumor supresor u karcinomu dojke, NISCH, ispoljava efekat preko RAC signalnog puta, koji je ključan u regulaciji hematopoeze i uključen u patogenezu mijeloidnih maligniteta, i smatra se atraktivnim targetom za ciljanu terapiju. Poznavanjefunkcionalnih domena NISCH-a će omogućiti dizajn i optimizaciju jedinjenja sa antikancerskim delovanjem...
AB  - Myeloid malignancies are clonal diseases of myeloid cell line, which arise, primary, as a consequence of aberrant self-renewal and differentiation of hematopoietic stem cells. Annual incidence in Europe is 7.5-8.6/100 000 and the mean 5-year survival rate, with standard therapy, is 37%. Since malignancies often carry more than one driver mutation, with high intra- and inter-cancer genetic heterogeneity, there is a need for individualized therapeutic approach to each patient. Personalized medicine is based on the principle of achieving maximal therapeutic efficacy through the use of targeted drugs for biologically characterized patients. Bioinformatics has an important role in identification of molecules for targeted therapy, rational drug design and identification of informative biomarkers for companion diagnostics.The subject of this research included genes and their protein products that have important roles in the pathogenesis of myeloid malignancies. WT1 is a transcription factor that controls the expression of genes involved in apoptosis, proliferation and differentiation. This gene is overexpressed in 70-90%, mutated in ~10% of acute myeloid leukemia (AML) and it is engaged in more than 40 time- and context-dependant protein-protein interactions. Characterizing entire network of WT1 cofactors can improve identification of new molecules and their interactions that could be targets for therapy of AML.Recently identified tumor suppressor in breast cancer, NISCH, affects RAC signaling pathway, which is important in regulation of hematopoiesis and has a role in pathogenesis of myeloid malignancies, so it is considered an attractive target for targeted therapy.Characterization of functional domains of NISCH can enable design and optimization of compounds with anticancer effect...
PB  - Универзитет у Београду, Биолошки факултет
T2  - Универзитет у Београду
T1  - Bioinformatička analiza proteina uključenih u patogenezu mijeloidnih maligniteta
T1  - Bioinformatics analysis of proteins involved in pathogenesis of myeloid malignancies
UR  - https://hdl.handle.net/21.15107/rcub_nardus_4929
ER  - 

@phdthesis{
author = "Gemović, Branislava S.",
year = "2015",
abstract = "Mijeloidni maligniteti su klonalne bolesti ćelija mijeloidne krvne loze, koje su, pre svega, posledica poremećaja samoobnavljanja i diferencijacije hematopoetskih matičnih ćelija. Godišnja incidenca u Evropi je 7,5-8,6/100 000, a prosečna petogodišnja stopa preživljavanja pacijenata, uz primenu standardne terapije, je 37%. S obzirom da maligniteti najčešće nose više od jedne vodeće – ‘driver’ mutacije, pri čemu je velika intra- i interkancerska genetička heterogenost, postoji potreba za individualizovanim terapeutskim pristupom svakom pacijentu. Personalizovana medicina označava princip postizanja maksimalne terapijske efikasnosti kroz upotrebu ciljanih terapeutika kod biološki okarakterisanih pacijenata. Bioinformatika ima značajnu ulogu u identifikaciji ciljnih molekula za razvoj terapije, racionalnom dizajnu lekova i identifikaciji informativnih biomarkera za prateću dijagnostiku.Predmet istraživanja u ovom radu bili su geni i njihovi proteinski produkti koji imaju važnu ulogu u patogenezi mijeloidnih maligniteta. WT1 je transkripcioni faktor, koji kontroliše ekspresiju gena uključenih u apoptozu, proliferaciju i diferencijaciju. Ovaj gen ima povećanu ekspresiju u 70-90%, a mutiran je u ~10% akutnih mijeloidnih leukemija (AML) i učestvuje u preko 40 protein-protein interakcija, koje su vremenski i kontekstno zavisne. Mapiranje celokupne mreže WT1 kofaktora doprineće identifikaciji novih molekula i interakcija, koje su potencijalni targeti za ciljanu terapiju AML.Nedavno otkriven tumor supresor u karcinomu dojke, NISCH, ispoljava efekat preko RAC signalnog puta, koji je ključan u regulaciji hematopoeze i uključen u patogenezu mijeloidnih maligniteta, i smatra se atraktivnim targetom za ciljanu terapiju. Poznavanjefunkcionalnih domena NISCH-a će omogućiti dizajn i optimizaciju jedinjenja sa antikancerskim delovanjem..., Myeloid malignancies are clonal diseases of myeloid cell line, which arise, primary, as a consequence of aberrant self-renewal and differentiation of hematopoietic stem cells. Annual incidence in Europe is 7.5-8.6/100 000 and the mean 5-year survival rate, with standard therapy, is 37%. Since malignancies often carry more than one driver mutation, with high intra- and inter-cancer genetic heterogeneity, there is a need for individualized therapeutic approach to each patient. Personalized medicine is based on the principle of achieving maximal therapeutic efficacy through the use of targeted drugs for biologically characterized patients. Bioinformatics has an important role in identification of molecules for targeted therapy, rational drug design and identification of informative biomarkers for companion diagnostics.The subject of this research included genes and their protein products that have important roles in the pathogenesis of myeloid malignancies. WT1 is a transcription factor that controls the expression of genes involved in apoptosis, proliferation and differentiation. This gene is overexpressed in 70-90%, mutated in ~10% of acute myeloid leukemia (AML) and it is engaged in more than 40 time- and context-dependant protein-protein interactions. Characterizing entire network of WT1 cofactors can improve identification of new molecules and their interactions that could be targets for therapy of AML.Recently identified tumor suppressor in breast cancer, NISCH, affects RAC signaling pathway, which is important in regulation of hematopoiesis and has a role in pathogenesis of myeloid malignancies, so it is considered an attractive target for targeted therapy.Characterization of functional domains of NISCH can enable design and optimization of compounds with anticancer effect...",
publisher = "Универзитет у Београду, Биолошки факултет",
journal = "Универзитет у Београду",
title = "Bioinformatička analiza proteina uključenih u patogenezu mijeloidnih maligniteta, Bioinformatics analysis of proteins involved in pathogenesis of myeloid malignancies",
url = "https://hdl.handle.net/21.15107/rcub_nardus_4929"
}

Gemović, B. S.. (2015). Bioinformatička analiza proteina uključenih u patogenezu mijeloidnih maligniteta. in Универзитет у Београду
Универзитет у Београду, Биолошки факултет..
https://hdl.handle.net/21.15107/rcub_nardus_4929

Gemović BS. Bioinformatička analiza proteina uključenih u patogenezu mijeloidnih maligniteta. in Универзитет у Београду. 2015;.
https://hdl.handle.net/21.15107/rcub_nardus_4929 .

Gemović, Branislava S., "Bioinformatička analiza proteina uključenih u patogenezu mijeloidnih maligniteta" in Универзитет у Београду (2015),
https://hdl.handle.net/21.15107/rcub_nardus_4929 .

TY  - JOUR
AU  - Senćanski, Milan V.
AU  - Radošević, Draginja
AU  - Perović, Vladimir R.
AU  - Gemović, Branislava S.
AU  - Stanojevic, Maja
AU  - Veljković, Nevena V.
AU  - Glišić, Sanja
PY  - 2015
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/806
AB  - The influenza virus represents a permanent global health threat because it circulates not only within but also between numerous host populations, thereby frequently causing unexpected outbreaks in animals and humans with a generally unpredictable course of disease and epidemiology. Conventional influenza therapy is directed against the viral neuraminidase protein, which promotes virus release from infected cells, and the viral ion channel M2, which facilitates viral uncoating. However, these drugs, albeit effective, have a major drawback: their targets are of a highly variable sequence. As a consequence, the virus can readily acquire resistance by mutating the drug targets. Indeed, most seasonal A/H1N1 viruses and the 2009 H1N1 virus are resistant to M2 inhibitors, and a significant proportion of the seasonal A/H1N1 viruses are resistant to the neuraminidase inhibitor oseltamivir. Development of new effective drugs for treatment of disease during the regular influenza seasons and the possible influenza pandemic represents an important goal. The results presented here point out natural products as a promising source of low toxic and widely accessible drug candidates for treatment of the influenza disease. Natural products combined with new therapeutic targets and drug repurposing techniques, which accelerate development of new drugs, serve as an important platform for development of new influenza therapeutics.
T2  - Current Pharmaceutical Design
T1  - Natural Products as Promising Therapeutics for Treatment of Influenza Disease
VL  - 21
IS  - 38
SP  - 5573
EP  - 5588
DO  - 10.2174/1381612821666151002113426
ER  - 

@article{
author = "Senćanski, Milan V. and Radošević, Draginja and Perović, Vladimir R. and Gemović, Branislava S. and Stanojevic, Maja and Veljković, Nevena V. and Glišić, Sanja",
year = "2015",
abstract = "The influenza virus represents a permanent global health threat because it circulates not only within but also between numerous host populations, thereby frequently causing unexpected outbreaks in animals and humans with a generally unpredictable course of disease and epidemiology. Conventional influenza therapy is directed against the viral neuraminidase protein, which promotes virus release from infected cells, and the viral ion channel M2, which facilitates viral uncoating. However, these drugs, albeit effective, have a major drawback: their targets are of a highly variable sequence. As a consequence, the virus can readily acquire resistance by mutating the drug targets. Indeed, most seasonal A/H1N1 viruses and the 2009 H1N1 virus are resistant to M2 inhibitors, and a significant proportion of the seasonal A/H1N1 viruses are resistant to the neuraminidase inhibitor oseltamivir. Development of new effective drugs for treatment of disease during the regular influenza seasons and the possible influenza pandemic represents an important goal. The results presented here point out natural products as a promising source of low toxic and widely accessible drug candidates for treatment of the influenza disease. Natural products combined with new therapeutic targets and drug repurposing techniques, which accelerate development of new drugs, serve as an important platform for development of new influenza therapeutics.",
journal = "Current Pharmaceutical Design",
title = "Natural Products as Promising Therapeutics for Treatment of Influenza Disease",
volume = "21",
number = "38",
pages = "5573-5588",
doi = "10.2174/1381612821666151002113426"
}

Senćanski, M. V., Radošević, D., Perović, V. R., Gemović, B. S., Stanojevic, M., Veljković, N. V.,& Glišić, S.. (2015). Natural Products as Promising Therapeutics for Treatment of Influenza Disease. in Current Pharmaceutical Design, 21(38), 5573-5588.
https://doi.org/10.2174/1381612821666151002113426

Senćanski MV, Radošević D, Perović VR, Gemović BS, Stanojevic M, Veljković NV, Glišić S. Natural Products as Promising Therapeutics for Treatment of Influenza Disease. in Current Pharmaceutical Design. 2015;21(38):5573-5588.
doi:10.2174/1381612821666151002113426 .

Senćanski, Milan V., Radošević, Draginja, Perović, Vladimir R., Gemović, Branislava S., Stanojevic, Maja, Veljković, Nevena V., Glišić, Sanja, "Natural Products as Promising Therapeutics for Treatment of Influenza Disease" in Current Pharmaceutical Design, 21, no. 38 (2015):5573-5588,
https://doi.org/10.2174/1381612821666151002113426 . .

TY  - JOUR
AU  - Vujicic, Ana Djordjevic
AU  - Gemović, Branislava S.
AU  - Veljković, Veljko
AU  - Glišić, Sanja
AU  - Veljković, Nevena V.
PY  - 2014
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/5960
AB  - Background/Aim. High sera reactivity with a peptide derived from human immunodeficiency virus HIV-I envelope protein gp120, NMI., correlate with non-progressive HIV-1 infection and also may have protective role in breast and prostate cancer. We also detected a low NTM1 reactive antibodies titer in healthy HIV negative sera and showed that antibody levels can be significantly increased with vigorous physical activity. However, the immune system seems to be unresponsive or tolerant to this peptide, implicating that the NTM1 sequence encompasses or overlaps a certain innate immune epitope. The aim of this study was to present evidences that NTM1 binding antibodies are components of innate immune humoral response, by confirming their presence in sera of newborn babies. For this purpose we collected a set of 225 innate antigen sequences reported in the literature and screened it for candidate antigens with the highest sequence and spectral similarity to NTM1 derived from HIV-1 gp120. Methods. Sera from 18 newborns were tested using ELISA, with peptide NTM1. Sequences from innate antigen database were aligned by an EMBOSS Water bioinformatics tool. Results. We identified NTM1 reactive antibodies in sera of HIV negative newborn babies. Further, in order to identify which of already known innate antigens are the most similar to NTM1 peptide we screened innate immune antigen sequence database collected from the literature. This screening revealed that the most similar sequence are ribonucleoproteins RO60, in addition to previously identified N-terminus of vasoactive intestinal peptide. Conclusion. The results of this study confirm the hypothesis that NTM1 recognizing antibodies are a part of humoral innate immune response. Further, computational similarity screening revealed a vasoactive intestinal peptide and RO60 as the most similar sequences and the strongest candidate antigens. In the light of the presented results, it is appealing that testing blood reactivity at birth, with specific innate antigens, particularly a vasoactive intestinal peptide, can reveal the potential to develop- or boost protective immune response in breast and prostate cancer and HIV infection later in life.
T2  - Vojnosanitetski pregled
T1  - Natural autoantibodies in healthy neonatals recognizing a peptide derived from the second conserved region of HIV-1 gp120
VL  - 71
IS  - 4
SP  - 352
EP  - 361
DO  - 10.2298/VSP1404352D
ER  - 

@article{
author = "Vujicic, Ana Djordjevic and Gemović, Branislava S. and Veljković, Veljko and Glišić, Sanja and Veljković, Nevena V.",
year = "2014",
abstract = "Background/Aim. High sera reactivity with a peptide derived from human immunodeficiency virus HIV-I envelope protein gp120, NMI., correlate with non-progressive HIV-1 infection and also may have protective role in breast and prostate cancer. We also detected a low NTM1 reactive antibodies titer in healthy HIV negative sera and showed that antibody levels can be significantly increased with vigorous physical activity. However, the immune system seems to be unresponsive or tolerant to this peptide, implicating that the NTM1 sequence encompasses or overlaps a certain innate immune epitope. The aim of this study was to present evidences that NTM1 binding antibodies are components of innate immune humoral response, by confirming their presence in sera of newborn babies. For this purpose we collected a set of 225 innate antigen sequences reported in the literature and screened it for candidate antigens with the highest sequence and spectral similarity to NTM1 derived from HIV-1 gp120. Methods. Sera from 18 newborns were tested using ELISA, with peptide NTM1. Sequences from innate antigen database were aligned by an EMBOSS Water bioinformatics tool. Results. We identified NTM1 reactive antibodies in sera of HIV negative newborn babies. Further, in order to identify which of already known innate antigens are the most similar to NTM1 peptide we screened innate immune antigen sequence database collected from the literature. This screening revealed that the most similar sequence are ribonucleoproteins RO60, in addition to previously identified N-terminus of vasoactive intestinal peptide. Conclusion. The results of this study confirm the hypothesis that NTM1 recognizing antibodies are a part of humoral innate immune response. Further, computational similarity screening revealed a vasoactive intestinal peptide and RO60 as the most similar sequences and the strongest candidate antigens. In the light of the presented results, it is appealing that testing blood reactivity at birth, with specific innate antigens, particularly a vasoactive intestinal peptide, can reveal the potential to develop- or boost protective immune response in breast and prostate cancer and HIV infection later in life.",
journal = "Vojnosanitetski pregled",
title = "Natural autoantibodies in healthy neonatals recognizing a peptide derived from the second conserved region of HIV-1 gp120",
volume = "71",
number = "4",
pages = "352-361",
doi = "10.2298/VSP1404352D"
}

Vujicic, A. D., Gemović, B. S., Veljković, V., Glišić, S.,& Veljković, N. V.. (2014). Natural autoantibodies in healthy neonatals recognizing a peptide derived from the second conserved region of HIV-1 gp120. in Vojnosanitetski pregled, 71(4), 352-361.
https://doi.org/10.2298/VSP1404352D

Vujicic AD, Gemović BS, Veljković V, Glišić S, Veljković NV. Natural autoantibodies in healthy neonatals recognizing a peptide derived from the second conserved region of HIV-1 gp120. in Vojnosanitetski pregled. 2014;71(4):352-361.
doi:10.2298/VSP1404352D .

Vujicic, Ana Djordjevic, Gemović, Branislava S., Veljković, Veljko, Glišić, Sanja, Veljković, Nevena V., "Natural autoantibodies in healthy neonatals recognizing a peptide derived from the second conserved region of HIV-1 gp120" in Vojnosanitetski pregled, 71, no. 4 (2014):352-361,
https://doi.org/10.2298/VSP1404352D . .

TY  - CONF
AU  - Gemović, Branislava S.
AU  - Perović, Vladimir R.
AU  - Glišić, Sanja
AU  - Veljković, Nevena V.
PY  - 2014
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/7074
C3  - FEBS Journal
T1  - Predicting protein function from sequence and co-expression: preliminary results for breast cancer molecular markers
VL  - 281
SP  - 633
EP  - 634
UR  - https://hdl.handle.net/21.15107/rcub_vinar_7074
ER  - 

@conference{
author = "Gemović, Branislava S. and Perović, Vladimir R. and Glišić, Sanja and Veljković, Nevena V.",
year = "2014",
journal = "FEBS Journal",
title = "Predicting protein function from sequence and co-expression: preliminary results for breast cancer molecular markers",
volume = "281",
pages = "633-634",
url = "https://hdl.handle.net/21.15107/rcub_vinar_7074"
}

Gemović, B. S., Perović, V. R., Glišić, S.,& Veljković, N. V.. (2014). Predicting protein function from sequence and co-expression: preliminary results for breast cancer molecular markers. in FEBS Journal, 281, 633-634.
https://hdl.handle.net/21.15107/rcub_vinar_7074

Gemović BS, Perović VR, Glišić S, Veljković NV. Predicting protein function from sequence and co-expression: preliminary results for breast cancer molecular markers. in FEBS Journal. 2014;281:633-634.
https://hdl.handle.net/21.15107/rcub_vinar_7074 .

Gemović, Branislava S., Perović, Vladimir R., Glišić, Sanja, Veljković, Nevena V., "Predicting protein function from sequence and co-expression: preliminary results for breast cancer molecular markers" in FEBS Journal, 281 (2014):633-634,
https://hdl.handle.net/21.15107/rcub_vinar_7074 .

TY  - CONF
AU  - Gemović, Branislava S.
AU  - Perović, Vladimir R.
AU  - Glišić, Sanja
AU  - Veljković, Nevena V.
PY  - 2014
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/10771
AB  - The most common genetic variations in humans are Single Nucleotide Polymorphisms (SNPs), so predicting their associations with cancers is a significant issue. Here, we were particularly interested in SNPs occurring outside protein Conserved Domains (CDs) of TET2, a recently discovered epigenetic regulator involved in leukemogenesis. Functional effects of TET2 gene variations were assessed with four publicly available tools: PhD-SNP, MutPred, PolyPhen-2 and SIFT. The methods were tested on the dataset of 166 SNPs and somatic TET2 mutations, and separately on the subset of 69 variations outside TET2 CDs. Abilities of tested tools to separate neutral SNPs from pathogenic mutations were similar to previously reported on complete TET2 dataset. However, we observed significantly lower accuracy of predictions outside CDs, ranging from 0.54 to 0.62. Also, areas under the ROC curves were low, 0.51-0.55. Correlations between predictions and positions of variations inside/outside CDs were significant and high, 0.46-0.78. Low efficiency of commonly used tools in predicting functional effects of variations outside CDs emphasize the need for new or modified algorithms.
C3  - 2nd International Conference “Theoretical Approaches to Bioinformation Systems” (TABIS.2013), September 17-22, 2013, Belgrade, Serbia
T1  - Can We Use Standard Tools to Predict Functional Effects of Missense Gene Variations Outside Conserved Domains? TET2 Example
SP  - 65
EP  - 71
UR  - https://hdl.handle.net/21.15107/rcub_vinar_10771
ER  - 

@conference{
author = "Gemović, Branislava S. and Perović, Vladimir R. and Glišić, Sanja and Veljković, Nevena V.",
year = "2014",
abstract = "The most common genetic variations in humans are Single Nucleotide Polymorphisms (SNPs), so predicting their associations with cancers is a significant issue. Here, we were particularly interested in SNPs occurring outside protein Conserved Domains (CDs) of TET2, a recently discovered epigenetic regulator involved in leukemogenesis. Functional effects of TET2 gene variations were assessed with four publicly available tools: PhD-SNP, MutPred, PolyPhen-2 and SIFT. The methods were tested on the dataset of 166 SNPs and somatic TET2 mutations, and separately on the subset of 69 variations outside TET2 CDs. Abilities of tested tools to separate neutral SNPs from pathogenic mutations were similar to previously reported on complete TET2 dataset. However, we observed significantly lower accuracy of predictions outside CDs, ranging from 0.54 to 0.62. Also, areas under the ROC curves were low, 0.51-0.55. Correlations between predictions and positions of variations inside/outside CDs were significant and high, 0.46-0.78. Low efficiency of commonly used tools in predicting functional effects of variations outside CDs emphasize the need for new or modified algorithms.",
journal = "2nd International Conference “Theoretical Approaches to Bioinformation Systems” (TABIS.2013), September 17-22, 2013, Belgrade, Serbia",
title = "Can We Use Standard Tools to Predict Functional Effects of Missense Gene Variations Outside Conserved Domains? TET2 Example",
pages = "65-71",
url = "https://hdl.handle.net/21.15107/rcub_vinar_10771"
}

Gemović, B. S., Perović, V. R., Glišić, S.,& Veljković, N. V.. (2014). Can We Use Standard Tools to Predict Functional Effects of Missense Gene Variations Outside Conserved Domains? TET2 Example. in 2nd International Conference “Theoretical Approaches to Bioinformation Systems” (TABIS.2013), September 17-22, 2013, Belgrade, Serbia, 65-71.
https://hdl.handle.net/21.15107/rcub_vinar_10771

Gemović BS, Perović VR, Glišić S, Veljković NV. Can We Use Standard Tools to Predict Functional Effects of Missense Gene Variations Outside Conserved Domains? TET2 Example. in 2nd International Conference “Theoretical Approaches to Bioinformation Systems” (TABIS.2013), September 17-22, 2013, Belgrade, Serbia. 2014;:65-71.
https://hdl.handle.net/21.15107/rcub_vinar_10771 .

Gemović, Branislava S., Perović, Vladimir R., Glišić, Sanja, Veljković, Nevena V., "Can We Use Standard Tools to Predict Functional Effects of Missense Gene Variations Outside Conserved Domains? TET2 Example" in 2nd International Conference “Theoretical Approaches to Bioinformation Systems” (TABIS.2013), September 17-22, 2013, Belgrade, Serbia (2014):65-71,
https://hdl.handle.net/21.15107/rcub_vinar_10771 .

TY  - JOUR
AU  - Nikolić, Katarina M.
AU  - Veljković, Nevena V.
AU  - Gemović, Branislava S.
AU  - Srdić-Rajić, Tatjana
AU  - Agbaba, Danica
PY  - 2013
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/5398
AB  - The group of imidazoline-1 receptors (I-1-IR) agonists encompasses drugs are currently used in treatment of high blood pressure and hyperglycemia. The I-1-IR protein structures have not been determined yet, but Nischarin protein that binds numerous imidazoline ligands inducing initiation of various cell-signaling cascades, including apoptosis, is identified as strong I-1-IR candidate. In this study we examined apoptotic activity of rilmenidine (potent I-1-IR agonist), moxonidine (moderate I-1-IR agonist), and efaroxan (I-1-IR partial agonist) on cancer cell line (K562) expressing Nischarin. The Nischarine domains mapping was performed by use of the Informational Spectrum Method (ISM). The 3D-Quantitative Structure-Activity Relationship (3D-QSAR) and virtual docking studies of 29 I-1-IR ligands (agonists, partial agonists, and antagonists) were carried out on I-1-IR receptors binding affinities. The 3D-QSAR study defined 3D-pharmacophore models for I-1-IR agonistic and I-1-IR antagonistic activity and created regression model for prediction of I-1-IR activity of novel compounds. The 3D-QSAR models were applied for design and evaluation of novel I-1-IR agonists and I-1-IR antagonists. The most promising I-1-IR ligands with enhanced activities than parent compounds were proposed for synthesis. The results of 3D-QSAR, ISM, and virtual docking studies were in perfect agreement and allowed precise definition of binding mode of I-1-IR agonists (Arg 758, Arg 866, Val 981, and Glu 1057) and significantly different binding modes of I-1-IR antagonists or partial I-1-IR agonists. The performed theoretical study provides reliable system for evaluation of I-1-IR agonistic and I-1-IR antagonistic activity of novel I-1-IR ligands, as drug candidates with anticancer activities.
T2  - Combinatorial Chemistry and High Throughput Screening
T1  - Imidazoline-1 Receptor Ligands as Apoptotic Agents: Pharmacophore Modeling and Virtual Docking Study
VL  - 16
IS  - 4
SP  - 298
EP  - 319
DO  - 10.2174/1386207311316040004
ER  - 

@article{
author = "Nikolić, Katarina M. and Veljković, Nevena V. and Gemović, Branislava S. and Srdić-Rajić, Tatjana and Agbaba, Danica",
year = "2013",
abstract = "The group of imidazoline-1 receptors (I-1-IR) agonists encompasses drugs are currently used in treatment of high blood pressure and hyperglycemia. The I-1-IR protein structures have not been determined yet, but Nischarin protein that binds numerous imidazoline ligands inducing initiation of various cell-signaling cascades, including apoptosis, is identified as strong I-1-IR candidate. In this study we examined apoptotic activity of rilmenidine (potent I-1-IR agonist), moxonidine (moderate I-1-IR agonist), and efaroxan (I-1-IR partial agonist) on cancer cell line (K562) expressing Nischarin. The Nischarine domains mapping was performed by use of the Informational Spectrum Method (ISM). The 3D-Quantitative Structure-Activity Relationship (3D-QSAR) and virtual docking studies of 29 I-1-IR ligands (agonists, partial agonists, and antagonists) were carried out on I-1-IR receptors binding affinities. The 3D-QSAR study defined 3D-pharmacophore models for I-1-IR agonistic and I-1-IR antagonistic activity and created regression model for prediction of I-1-IR activity of novel compounds. The 3D-QSAR models were applied for design and evaluation of novel I-1-IR agonists and I-1-IR antagonists. The most promising I-1-IR ligands with enhanced activities than parent compounds were proposed for synthesis. The results of 3D-QSAR, ISM, and virtual docking studies were in perfect agreement and allowed precise definition of binding mode of I-1-IR agonists (Arg 758, Arg 866, Val 981, and Glu 1057) and significantly different binding modes of I-1-IR antagonists or partial I-1-IR agonists. The performed theoretical study provides reliable system for evaluation of I-1-IR agonistic and I-1-IR antagonistic activity of novel I-1-IR ligands, as drug candidates with anticancer activities.",
journal = "Combinatorial Chemistry and High Throughput Screening",
title = "Imidazoline-1 Receptor Ligands as Apoptotic Agents: Pharmacophore Modeling and Virtual Docking Study",
volume = "16",
number = "4",
pages = "298-319",
doi = "10.2174/1386207311316040004"
}

Nikolić, K. M., Veljković, N. V., Gemović, B. S., Srdić-Rajić, T.,& Agbaba, D.. (2013). Imidazoline-1 Receptor Ligands as Apoptotic Agents: Pharmacophore Modeling and Virtual Docking Study. in Combinatorial Chemistry and High Throughput Screening, 16(4), 298-319.
https://doi.org/10.2174/1386207311316040004

Nikolić KM, Veljković NV, Gemović BS, Srdić-Rajić T, Agbaba D. Imidazoline-1 Receptor Ligands as Apoptotic Agents: Pharmacophore Modeling and Virtual Docking Study. in Combinatorial Chemistry and High Throughput Screening. 2013;16(4):298-319.
doi:10.2174/1386207311316040004 .

Nikolić, Katarina M., Veljković, Nevena V., Gemović, Branislava S., Srdić-Rajić, Tatjana, Agbaba, Danica, "Imidazoline-1 Receptor Ligands as Apoptotic Agents: Pharmacophore Modeling and Virtual Docking Study" in Combinatorial Chemistry and High Throughput Screening, 16, no. 4 (2013):298-319,
https://doi.org/10.2174/1386207311316040004 . .

TY  - JOUR
AU  - Gemović, Branislava S.
AU  - Perović, Vladimir R.
AU  - Glišić, Sanja
AU  - Veljković, Nevena V.
PY  - 2013
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/5770
AB  - There are more than 500 amino acid substitutions in each human genome, and bioinformatics tools irreplaceably contribute to determination of their functional effects. We have developed feature-based algorithm for the detection of mutations outside conserved functional domains (CFDs) and compared its classification efficacy with the most commonly used phylogeny-based tools, PolyPhen-2 and SIFT. The new algorithm is based on the informational spectrum method (ISM), a feature-based technique, and statistical analysis. Our dataset contained neutral polymorphisms and mutations associated with myeloid malignancies from epigenetic regulators ASXL1, DNMT3A, EZH2, and TET2. PolyPhen-2 and SIFT had significantly lower accuracies in predicting the effects of amino acid substitutions outside CFDs than expected, with especially low sensitivity. On the other hand, only ISM algorithm showed statistically significant classification of these sequences. It outperformed PolyPhen-2 and SIFT by 15% and 13%, respectively. These results suggest that feature-based methods, like ISM, are more suitable for the classification of amino acid substitutions outside CFDs than phylogeny-based tools.
T2  - Scientific World Journal
T1  - Feature-Based Classification of Amino Acid Substitutions outside Conserved Functional Protein Domains
DO  - 10.1155/2013/948617
ER  - 

@article{
author = "Gemović, Branislava S. and Perović, Vladimir R. and Glišić, Sanja and Veljković, Nevena V.",
year = "2013",
abstract = "There are more than 500 amino acid substitutions in each human genome, and bioinformatics tools irreplaceably contribute to determination of their functional effects. We have developed feature-based algorithm for the detection of mutations outside conserved functional domains (CFDs) and compared its classification efficacy with the most commonly used phylogeny-based tools, PolyPhen-2 and SIFT. The new algorithm is based on the informational spectrum method (ISM), a feature-based technique, and statistical analysis. Our dataset contained neutral polymorphisms and mutations associated with myeloid malignancies from epigenetic regulators ASXL1, DNMT3A, EZH2, and TET2. PolyPhen-2 and SIFT had significantly lower accuracies in predicting the effects of amino acid substitutions outside CFDs than expected, with especially low sensitivity. On the other hand, only ISM algorithm showed statistically significant classification of these sequences. It outperformed PolyPhen-2 and SIFT by 15% and 13%, respectively. These results suggest that feature-based methods, like ISM, are more suitable for the classification of amino acid substitutions outside CFDs than phylogeny-based tools.",
journal = "Scientific World Journal",
title = "Feature-Based Classification of Amino Acid Substitutions outside Conserved Functional Protein Domains",
doi = "10.1155/2013/948617"
}

Gemović, B. S., Perović, V. R., Glišić, S.,& Veljković, N. V.. (2013). Feature-Based Classification of Amino Acid Substitutions outside Conserved Functional Protein Domains. in Scientific World Journal.
https://doi.org/10.1155/2013/948617

Gemović BS, Perović VR, Glišić S, Veljković NV. Feature-Based Classification of Amino Acid Substitutions outside Conserved Functional Protein Domains. in Scientific World Journal. 2013;.
doi:10.1155/2013/948617 .

Gemović, Branislava S., Perović, Vladimir R., Glišić, Sanja, Veljković, Nevena V., "Feature-Based Classification of Amino Acid Substitutions outside Conserved Functional Protein Domains" in Scientific World Journal (2013),
https://doi.org/10.1155/2013/948617 . .

TY  - JOUR
AU  - Glišić, Sanja
AU  - Veljković, Nevena V.
AU  - Jovanović-Ćupić, Snežana P.
AU  - Vasiljevic, Nada
AU  - Prljić, Jelena
AU  - Gemović, Branislava S.
AU  - Perović, Vladimir R.
AU  - Veljković, Veljko
PY  - 2012
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/4755
AB  - Hepatitis C virus (HCV) infection is a major and rising global health problem, affecting about 170 million people worldwide. The current standard of care treatment with interferon alpha and ribavirin in patients with the genotype 1 infection, the most frequent genotype in the USA and Western Europe, leads to a successful outcome in only about 50% of individuals. Accurate prediction of hepatitis C treatment response is of great benefit to patients and clinicians. The informational spectrum method, a virtual spectroscopy method for structure/function analysis of nucleotide and protein sequences, is applied here for the identification of the conserved information of the HCV proteins that correlate with the combination therapy outcome. Among the HCV proteins that we have analyzed the informational property of the p7 of HCV genotype 1b was best related to the therapy outcome. On the basis of these results, a simple bioinformatics criterion that could be useful in assessment of the response of HCV-infected patients to the combination therapy has been proposed.
T2  - Protein Journal
T1  - Assessment of Hepatitis C Virus Protein Sequences with Regard to Interferon/Ribavirin Combination Therapy Response in Patients with HCV Genotype 1b
VL  - 31
IS  - 2
SP  - 129
EP  - 136
DO  - 10.1007/s10930-011-9381-6
ER  - 

@article{
author = "Glišić, Sanja and Veljković, Nevena V. and Jovanović-Ćupić, Snežana P. and Vasiljevic, Nada and Prljić, Jelena and Gemović, Branislava S. and Perović, Vladimir R. and Veljković, Veljko",
year = "2012",
abstract = "Hepatitis C virus (HCV) infection is a major and rising global health problem, affecting about 170 million people worldwide. The current standard of care treatment with interferon alpha and ribavirin in patients with the genotype 1 infection, the most frequent genotype in the USA and Western Europe, leads to a successful outcome in only about 50% of individuals. Accurate prediction of hepatitis C treatment response is of great benefit to patients and clinicians. The informational spectrum method, a virtual spectroscopy method for structure/function analysis of nucleotide and protein sequences, is applied here for the identification of the conserved information of the HCV proteins that correlate with the combination therapy outcome. Among the HCV proteins that we have analyzed the informational property of the p7 of HCV genotype 1b was best related to the therapy outcome. On the basis of these results, a simple bioinformatics criterion that could be useful in assessment of the response of HCV-infected patients to the combination therapy has been proposed.",
journal = "Protein Journal",
title = "Assessment of Hepatitis C Virus Protein Sequences with Regard to Interferon/Ribavirin Combination Therapy Response in Patients with HCV Genotype 1b",
volume = "31",
number = "2",
pages = "129-136",
doi = "10.1007/s10930-011-9381-6"
}

Glišić, S., Veljković, N. V., Jovanović-Ćupić, S. P., Vasiljevic, N., Prljić, J., Gemović, B. S., Perović, V. R.,& Veljković, V.. (2012). Assessment of Hepatitis C Virus Protein Sequences with Regard to Interferon/Ribavirin Combination Therapy Response in Patients with HCV Genotype 1b. in Protein Journal, 31(2), 129-136.
https://doi.org/10.1007/s10930-011-9381-6

Glišić S, Veljković NV, Jovanović-Ćupić SP, Vasiljevic N, Prljić J, Gemović BS, Perović VR, Veljković V. Assessment of Hepatitis C Virus Protein Sequences with Regard to Interferon/Ribavirin Combination Therapy Response in Patients with HCV Genotype 1b. in Protein Journal. 2012;31(2):129-136.
doi:10.1007/s10930-011-9381-6 .

Glišić, Sanja, Veljković, Nevena V., Jovanović-Ćupić, Snežana P., Vasiljevic, Nada, Prljić, Jelena, Gemović, Branislava S., Perović, Vladimir R., Veljković, Veljko, "Assessment of Hepatitis C Virus Protein Sequences with Regard to Interferon/Ribavirin Combination Therapy Response in Patients with HCV Genotype 1b" in Protein Journal, 31, no. 2 (2012):129-136,
https://doi.org/10.1007/s10930-011-9381-6 . .

TY  - CONF
AU  - Gemović, Branislava S.
AU  - Perović, Vladimir R.
AU  - Glišić, Sanja
AU  - Veljković, Nevena V.
PY  - 2012
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/6973
C3  - FEBS Journal
T1  - Bioinformatics approach to protein-protein interactions of WT1 isoforms
VL  - 279
SP  - 295
EP  - 295
UR  - https://hdl.handle.net/21.15107/rcub_vinar_6973
ER  - 

@conference{
author = "Gemović, Branislava S. and Perović, Vladimir R. and Glišić, Sanja and Veljković, Nevena V.",
year = "2012",
journal = "FEBS Journal",
title = "Bioinformatics approach to protein-protein interactions of WT1 isoforms",
volume = "279",
pages = "295-295",
url = "https://hdl.handle.net/21.15107/rcub_vinar_6973"
}

Gemović, B. S., Perović, V. R., Glišić, S.,& Veljković, N. V.. (2012). Bioinformatics approach to protein-protein interactions of WT1 isoforms. in FEBS Journal, 279, 295-295.
https://hdl.handle.net/21.15107/rcub_vinar_6973

Gemović BS, Perović VR, Glišić S, Veljković NV. Bioinformatics approach to protein-protein interactions of WT1 isoforms. in FEBS Journal. 2012;279:295-295.
https://hdl.handle.net/21.15107/rcub_vinar_6973 .

Gemović, Branislava S., Perović, Vladimir R., Glišić, Sanja, Veljković, Nevena V., "Bioinformatics approach to protein-protein interactions of WT1 isoforms" in FEBS Journal, 279 (2012):295-295,
https://hdl.handle.net/21.15107/rcub_vinar_6973 .