Biologically enhanced machine learning model to uncover novel gene-drug targets for alzheimer s disease
Abstract
Given the complexity and multifactorial nature of Alzheimers disease, investigating potential drug-gene targets is imperative for developing effective therapies and advancing our understanding of the underlying mechanisms driving the disease. We present an explainable ML model that integrates the role and impact of gene interactions to drive the genomic variant feature selection. The model leverages both the Alzheimers knowledge base and the Drug-Gene interaction database (DGIdb) to identify a list of biologically plausible novel gene-drug targets for further investigation. Model validation is performed on an ethnically diverse study sample obtained from the Alzheimers Disease Sequencing Project (ADSP), a multi-ancestry multi-cohort genomic study. To mitigate population stratification and spurious associations from ML analysis, we implemented novel data curation methods. The study outcomes include a set of possible gene targets for further functional follow-up and drug repurposing.
Department(s)
Cooperative Engineering Program
Document Type
Conference Proceeding
DOI
10.1142/9789819807024-0032
Keywords
Alzheimers disease, epistasis, feature importance, genomics, informatics
Publication Date
1-1-2025
Recommended Citation
Obafemi-Ajayi, Tayo; Orlenko, Alena; Venkatesan, Mythreye; Shen, Li; Ritchie, Marylyn D.; Wang, Zhiping Paul; and Moore, Jason H., "Biologically enhanced machine learning model to uncover novel gene-drug targets for alzheimer s disease" (2025). Faculty Scholarship. 277.
https://bearworks.missouristate.edu/articles00/277
Journal Title
Pacific Symposium on Biocomputing
