Utilizing Genetic Programming to Enhance Polygenic Risk Score Calculation

Created by W.Langdon from gp-bibliography.bib Revision:1.7656

@InProceedings{Hurta:2023:BIBM,

• author = "Martin Hurta and Jana Schwarzerova and Thomas Naegele and Wolfram Weckwerth and Valentine Provaznik and Lukas Sekanina",
• booktitle = "2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)",
• title = "Utilizing Genetic Programming to Enhance Polygenic Risk Score Calculation",
• year = "2023",
• pages = "3782--3787",
• abstract = "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.",
• keywords = "genetic algorithms, genetic programming, Evolution (biology), Plants (biology), Sociology, Medical services, Data models, Polygenic risk score, Genetic Variations, Computational biology",
• DOI = "doi:10.1109/BIBM58861.2023.10385615",
• ISSN = "2156-1133",
• month = dec,
• notes = "Also known as \cite{10385615}",

}

Genetic Programming entries for Martin Hurta Jana Schwarzerova Thomas Naegele Wolfram Weckwerth Valentine Provaznik Lukas Sekanina

Citations