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Highly Interpretable Prediction Models for SNP Data

Topics: Integration and Analysis of Genomic and Proteomic Data

Robin Nunkesser

Affiliation: Hamm-Lippstadt University of Applied Sciences, Marker Allee 76–78, 59063 Hamm, Germany

Keyword(s): Interpretable Machine Learning, SNP Data, Genetic Programming, Simulation Study.

Abstract: Binary prediction models for SNP data are often used in genetic association studies. The models should be highly interpretable to help understand possible underlying biological mechanisms. logicFS, GPAS, and logicDT can yield highly interpretable prediction models. The automatic prevention of overfitting requires improvement, however. We propose using GPAS as a black box and applying an external method for automatic model selection. We present an approach using the GPAS algorithm as a black box and show initial results on simulated data. The simulation is designed to motivate research to extend GPAS with automatic model selection. Additionally, we give an outlook on further extensions of GPAS.

CC BY-NC-ND 4.0

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Paper citation in several formats:

Nunkesser, R. (2025). Highly Interpretable Prediction Models for SNP Data. In Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS; ISBN 978-989-758-731-3; ISSN 2184-4305, SciTePress, pages 555-562. DOI: 10.5220/0013137600003911

@conference{bioinformatics25,
author={Robin Nunkesser},
title={Highly Interpretable Prediction Models for SNP Data},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS},
year={2025},
pages={555-562},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013137600003911},
isbn={978-989-758-731-3},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS
TI - Highly Interpretable Prediction Models for SNP Data
SN - 978-989-758-731-3
IS - 2184-4305
AU - Nunkesser, R.
PY - 2025
SP - 555
EP - 562
DO - 10.5220/0013137600003911
PB - SciTePress