Highly Interpretable Prediction Models for SNP Data

Robin Nunkesser

2025

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.

Paper Citation

in Harvard Style

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, SciTePress, pages 555-562. DOI: 10.5220/0013137600003911

in Bibtex Style

@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},
}

in EndNote Style

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
AU - Nunkesser R.
PY - 2025
SP - 555
EP - 562
DO - 10.5220/0013137600003911
PB - SciTePress