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Open Access

Peer-reviewed

Research Article

Using machine learning to predict and analyze complex trait diseases: Lessons from a simple abstract model

Eden Maimon,

Roles Conceptualization, Software, Validation, Visualization, Writing – review & editing

Affiliation The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
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Ori Bondi,

Roles Conceptualization, Methodology, Software

Affiliation The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel
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John Moult,

Roles Conceptualization, Methodology, Supervision, Validation, Writing – review & editing

Affiliations Institute for Bioscience and Biotechnology Research, Rockville, Maryland, United States of America, Department of Cell Biology and Molecular Genetics, University of Maryland, College Park, Maryland, United States of America
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Ron Unger

Roles Conceptualization, Formal analysis, Resources, Supervision, Writing – original draft, Writing – review & editing

* E-mail: ron@biomodel.os.biu.ac.il

Affiliation The Mina and Everard Goodman Faculty of Life Sciences, Bar-Ilan University, Ramat-Gan, Israel

https://orcid.org/0000-0003-4153-3922

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Using machine learning to predict and analyze complex trait diseases: Lessons from a simple abstract model

Eden Maimon,
Ori Bondi,
John Moult,
Ron Unger

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Published: February 23, 2026
https://doi.org/10.1371/journal.pone.0342490

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Medical risk factors
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Genetics of disease
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Sign epistasis
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Genetic loci
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Machine learning algorithms
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Complex traits
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Epidemiology
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Algorithms
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