PathDisGene: Discovering Informative Gene Groups for Disease Diagnosis Using Pathway-Disease Associations and a Grouping, Scoring, Modeling-Based Machine Learning Approach

Emma Qumsiyeh, Burcu Bakir-Gungo, Malik Yousef

2025

Abstract

Recently, machine learning and various feature selection techniques have become popular for understanding the relationship between genes, molecular pathways, and diseases. Integrating existing domain knowledge into biological data analysis has demonstrated considerable potential for finding new biomarkers with translational uses. This paper presents PathDisGene, an innovative machine-learning tool that integrates existing domain knowledge by utilizing a Grouping-Scoring-Modeling (G-S-M) approach to discover associations among gene-pathway-disease. The first step in PathDisGene is the grouping component that associates genes according to their biological associations with diseases and pathways. This component uses the Comparative Toxicogenomics Database (CTD). Subsequently, the scoring component is applied to score each group and the highest-ranked groupings are then used to train the classifier. We test PathDisGene on ten GEO datasets and demonstrate its performance, where most of them are with high accuracy, sensitivity, specificity, and AUC values across various diseases. The tool's capacity to recognize new pathway-disease associations and uncover connections between pathways and diseases along their associated genes underscores its potential as a significant asset in promoting precision medicine and systems biology.

Paper Citation

in Harvard Style

Qumsiyeh E., Bakir-Gungo B. and Yousef M. (2025). PathDisGene: Discovering Informative Gene Groups for Disease Diagnosis Using Pathway-Disease Associations and a Grouping, Scoring, Modeling-Based Machine Learning Approach. 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 676-683. DOI: 10.5220/0013378200003911

in Bibtex Style

@conference{bioinformatics25,
author={Emma Qumsiyeh and Burcu Bakir-Gungo and Malik Yousef},
title={PathDisGene: Discovering Informative Gene Groups for Disease Diagnosis Using Pathway-Disease Associations and a Grouping, Scoring, Modeling-Based Machine Learning Approach},
booktitle={Proceedings of the 18th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS},
year={2025},
pages={676-683},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013378200003911},
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 - PathDisGene: Discovering Informative Gene Groups for Disease Diagnosis Using Pathway-Disease Associations and a Grouping, Scoring, Modeling-Based Machine Learning Approach
SN - 978-989-758-731-3
AU - Qumsiyeh E.
AU - Bakir-Gungo B.
AU - Yousef M.
PY - 2025
SP - 676
EP - 683
DO - 10.5220/0013378200003911
PB - SciTePress