Utilizing Genetic Programming to Enhance Polygenic Risk Score Calculation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F23%3APU149766" target="_blank" >RIV/00216305:26230/23:PU149766 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10385615" target="_blank" >https://ieeexplore.ieee.org/document/10385615</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/BIBM58861.2023.10385615" target="_blank" >10.1109/BIBM58861.2023.10385615</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Utilizing Genetic Programming to Enhance Polygenic Risk Score Calculation

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Utilizing Genetic Programming to Enhance Polygenic Risk Score Calculation

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2023

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

2023 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2023)

ISBN

979-8-3503-3748-8

ISSN

—

e-ISSN

—

Počet stran výsledku

6

Strana od-do

3782-3787

Název nakladatele

Institute of Electrical and Electronics Engineers

Místo vydání

Istanbul

Místo konání akce

Istanbul

Datum konání akce

5. 12. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—