Detail výsledku
Utilizing Genetic Programming to Enhance Polygenic Risk Score Calculation
Schwarzerová Jana, Ing. et Ing., MSc, UBMI (FEKT)
Nägele Thomas
Weckwerth Wolfram, Prof., Dr. rer. nat.
Provazník Valentýna, prof. Ing., Ph.D., UBMI (FEKT)
Sekanina Lukáš, prof. Ing., Ph.D., UPSY (FIT)
The polygenic risk score has proven to be a valuable tool for assessing an individual's genetic predisposition to phenotype (disease) within biomedicine in recent years. However, traditional regression-based methods for polygenic risk scores calculation have limitations that can impede their accuracy and predictive power. This study introduces an innovative approach to enhance polygenic risk scores calculation through the application of genetic programming. By harnessing the power of genetic programming, we aim to overcome the limitations of traditional regression techniques and improve the accuracy of polygenic risk scores predictions. Specifically, we showed that a polygenic risk score generated through Cartesian genetic programming yielded comparable or even more robust statistical distinctions between groups that we evaluated within three independent case studies.
Polygenic risk score, genetic variations, computational biology, genetic programming
@inproceedings{BUT185638,
author="Martin {Hurta} and Jana {Schwarzerová} and Thomas {Nägele} and Wolfram {Weckwerth} and Valentýna {Provazník} and Lukáš {Sekanina}",
title="Utilizing Genetic Programming to Enhance Polygenic Risk Score Calculation",
booktitle="2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2023)",
year="2023",
pages="3782--3787",
publisher="Institute of Electrical and Electronics Engineers",
address="Istanbul",
doi="10.1109/BIBM58861.2023.10385615",
isbn="979-8-3503-3748-8",
url="https://ieeexplore.ieee.org/document/10385615"
}