Utilizing Genetic Programming to Enhance Polygenic Risk Score Calculation
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @InProceedings{Hurta:2023:BIBM,
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author = "Martin Hurta and Jana Schwarzerova and
Thomas Naegele and Wolfram Weckwerth and Valentine Provaznik and
Lukas Sekanina",
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booktitle = "2023 IEEE International Conference on Bioinformatics
and Biomedicine (BIBM)",
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title = "Utilizing Genetic Programming to Enhance Polygenic
Risk Score Calculation",
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year = "2023",
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pages = "3782--3787",
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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.",
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keywords = "genetic algorithms, genetic programming, Evolution
(biology), Plants (biology), Sociology, Medical
services, Data models, Polygenic risk score, Genetic
Variations, Computational biology",
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DOI = "doi:10.1109/BIBM58861.2023.10385615",
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ISSN = "2156-1133",
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month = dec,
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notes = "Also known as \cite{10385615}",
- }
Genetic Programming entries for
Martin Hurta
Jana Schwarzerova
Thomas Naegele
Wolfram Weckwerth
Valentine Provaznik
Lukas Sekanina
Citations