TY  - JOUR
AU  - Pantić, Igor
AU  - Paunović, Jovana
AU  - Pejić, Snežana
AU  - Drakulić, Dunja R.
AU  - Todorović, Ana
AU  - Stanković, Sanja
AU  - Vučević, Danijela
AU  - Cumic, Jelena
AU  - Radosavljević, Tatjana
PY  - 2022
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/10212
AB  - Artificial intelligence (AI) and machine learning models are today frequently used for classification and prediction of various biochemical processes and phenomena. In recent years, numerous research efforts have been focused on developing such models for assessment, categorization, and prediction of oxidative stress. Supervised machine learning can successfully automate the process of evaluation and quantification of oxidative damage in biological samples, as well as extract useful data from the abundance of experimental results. In this concise review, we cover the possible applications of neural networks, decision trees and regression analysis as three common strategies in machine learning. We also review recent works on the various weaknesses and limitations of artificial intelligence in biochemistry and related scientific areas. Finally, we discuss future innovative approaches on the ways how AI can contribute to the automation of oxidative stress measurement and diagnosis of diseases associated with oxidative damage.
T2  - Chemico-Biological Interactions
T2  - Chemico-Biological Interactions
T1  - Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art
VL  - 358
SP  - 109888
DO  - 10.1016/j.cbi.2022.109888
ER  - 

@article{
author = "Pantić, Igor and Paunović, Jovana and Pejić, Snežana and Drakulić, Dunja R. and Todorović, Ana and Stanković, Sanja and Vučević, Danijela and Cumic, Jelena and Radosavljević, Tatjana",
year = "2022",
abstract = "Artificial intelligence (AI) and machine learning models are today frequently used for classification and prediction of various biochemical processes and phenomena. In recent years, numerous research efforts have been focused on developing such models for assessment, categorization, and prediction of oxidative stress. Supervised machine learning can successfully automate the process of evaluation and quantification of oxidative damage in biological samples, as well as extract useful data from the abundance of experimental results. In this concise review, we cover the possible applications of neural networks, decision trees and regression analysis as three common strategies in machine learning. We also review recent works on the various weaknesses and limitations of artificial intelligence in biochemistry and related scientific areas. Finally, we discuss future innovative approaches on the ways how AI can contribute to the automation of oxidative stress measurement and diagnosis of diseases associated with oxidative damage.",
journal = "Chemico-Biological Interactions, Chemico-Biological Interactions",
title = "Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art",
volume = "358",
pages = "109888",
doi = "10.1016/j.cbi.2022.109888"
}

Pantić, I., Paunović, J., Pejić, S., Drakulić, D. R., Todorović, A., Stanković, S., Vučević, D., Cumic, J.,& Radosavljević, T.. (2022). Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art. in Chemico-Biological Interactions, 358, 109888.
https://doi.org/10.1016/j.cbi.2022.109888

Pantić I, Paunović J, Pejić S, Drakulić DR, Todorović A, Stanković S, Vučević D, Cumic J, Radosavljević T. Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art. in Chemico-Biological Interactions. 2022;358:109888.
doi:10.1016/j.cbi.2022.109888 .

Pantić, Igor, Paunović, Jovana, Pejić, Snežana, Drakulić, Dunja R., Todorović, Ana, Stanković, Sanja, Vučević, Danijela, Cumic, Jelena, Radosavljević, Tatjana, "Artificial intelligence approaches to the biochemistry of oxidative stress: Current state of the art" in Chemico-Biological Interactions, 358 (2022):109888,
https://doi.org/10.1016/j.cbi.2022.109888 . .