Biologically Enhanced Machine Learning Model to uncover Novel Gene-Drug Targets for Alzheimer's Disease.

Journal: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
PMID: 39670388

Abstract

Given the complexity and multifactorial nature of Alzheimer's 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 Alzheimer's 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 Alzheimer's 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.

Authors

  • Alena Orlenko
    Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.
  • Mythreye Venkatesan
  • Li Shen
    Department of Clinical Pharmacy, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Marylyn D Ritchie
    From the Departments of Bioengineering (M.S.Y.), Radiology (H.S., N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K., W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104; Department of Radiology, Loyola University Medical Center, Maywood, Ill (A.D.G.); Department of Information Services, University of Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pa (A.B.).
  • Zhiping Paul Wang
  • Tayo Obafemi-Ajayi
  • Jason H Moore
    University of Pennsylvania, Philadelphia, PA, USA.

Keywords

External Resources

Popular Topics
Recent Journals