TY  - JOUR
AU  - Khanna, Narendra N
AU  - Singh, Manasvi
AU  - Maindarkar, Mahesh
AU  - Kumar, Ashish
AU  - Johri, Amer M.
AU  - Mentella, Laura
AU  - Laird, John R
AU  - Paraskevas, Kosmas I.
AU  - Ruzsa, Zoltan
AU  - Singh, Narpinder
AU  - Kalra, Mannudeep K.
AU  - Fernandes, Jose Fernandes E.
AU  - Chaturvedi, Seemant
AU  - Nicolaides, Andrew
AU  - Rathore, Vijay
AU  - Singh, Inder
AU  - Teji, Jagjit S.
AU  - Al-Maini, Mostafa
AU  - Isenović, Esma R.
AU  - Viswanathan, Vijay
AU  - Khanna, Puneet
AU  - Fouda, Mostafa M.
AU  - Saba, Luca
AU  - Suri, Jasjit S.
PY  - 2023
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/12112
AB  - Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.
T2  - Journal of Korean Medical Science
T1  - Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review
VL  - 38
IS  - 46
DO  - 10.3346/jkms.2023.38.e395
ER  - 

@article{
author = "Khanna, Narendra N and Singh, Manasvi and Maindarkar, Mahesh and Kumar, Ashish and Johri, Amer M. and Mentella, Laura and Laird, John R and Paraskevas, Kosmas I. and Ruzsa, Zoltan and Singh, Narpinder and Kalra, Mannudeep K. and Fernandes, Jose Fernandes E. and Chaturvedi, Seemant and Nicolaides, Andrew and Rathore, Vijay and Singh, Inder and Teji, Jagjit S. and Al-Maini, Mostafa and Isenović, Esma R. and Viswanathan, Vijay and Khanna, Puneet and Fouda, Mostafa M. and Saba, Luca and Suri, Jasjit S.",
year = "2023",
abstract = "Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.",
journal = "Journal of Korean Medical Science",
title = "Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review",
volume = "38",
number = "46",
doi = "10.3346/jkms.2023.38.e395"
}

Khanna, N. N., Singh, M., Maindarkar, M., Kumar, A., Johri, A. M., Mentella, L., Laird, J. R., Paraskevas, K. I., Ruzsa, Z., Singh, N., Kalra, M. K., Fernandes, J. F. E., Chaturvedi, S., Nicolaides, A., Rathore, V., Singh, I., Teji, J. S., Al-Maini, M., Isenović, E. R., Viswanathan, V., Khanna, P., Fouda, M. M., Saba, L.,& Suri, J. S.. (2023). Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review. in Journal of Korean Medical Science, 38(46).
https://doi.org/10.3346/jkms.2023.38.e395

Khanna NN, Singh M, Maindarkar M, Kumar A, Johri AM, Mentella L, Laird JR, Paraskevas KI, Ruzsa Z, Singh N, Kalra MK, Fernandes JFE, Chaturvedi S, Nicolaides A, Rathore V, Singh I, Teji JS, Al-Maini M, Isenović ER, Viswanathan V, Khanna P, Fouda MM, Saba L, Suri JS. Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review. in Journal of Korean Medical Science. 2023;38(46).
doi:10.3346/jkms.2023.38.e395 .

Khanna, Narendra N, Singh, Manasvi, Maindarkar, Mahesh, Kumar, Ashish, Johri, Amer M., Mentella, Laura, Laird, John R, Paraskevas, Kosmas I., Ruzsa, Zoltan, Singh, Narpinder, Kalra, Mannudeep K., Fernandes, Jose Fernandes E., Chaturvedi, Seemant, Nicolaides, Andrew, Rathore, Vijay, Singh, Inder, Teji, Jagjit S., Al-Maini, Mostafa, Isenović, Esma R., Viswanathan, Vijay, Khanna, Puneet, Fouda, Mostafa M., Saba, Luca, Suri, Jasjit S., "Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review" in Journal of Korean Medical Science, 38, no. 46 (2023),
https://doi.org/10.3346/jkms.2023.38.e395 . .