A random-walk-based learning framework to uncover novel gene candidates for Alzheimer's disease therapy

Authors

• Tayo Obafemi-Ajayi, Missouri State UniversityFollow
• Alena Orlenko, Cedars-Sinai Medical Center
• Binglan Li, Cedars-Sinai Medical Center
• Neda Khanjani, Cedars-Sinai Medical Center
• Mythreye Venkatesan, Cedars-Sinai Medical Center
• Li Shen, Epidemiology
• Marylyn D. Ritchie, University of Pennsylvania Perelman School of Medicine
• Zhiping Paul Wang, Cedars-Sinai Medical Center
• Jason H. Moore, Cedars-Sinai Medical Center

Abstract

Identifying repurposable therapeutic targets for Alzheimer's disease (AD) remains challenging due to various clinical and biological factors. This study aimed to identify candidate genes for AD therapy. We hypothesize that gene and disease-specific network properties-learnable from these large-scale biomedical knowledge graphs-can inform implicit gene-AD connections and prioritize repurposable AD drug targets. To evaluate the hypothesis, we focused on druggable genes curated from Drug-Gene Interaction Database and Alzheimer's Knowledge Base (AlzKB). We applied scalable random walk methods to Hetionet to learn unbiased gene and disease embeddings, representative of their topological and semantic network properties. The embeddings were then used to compute gene-AD similarity and derive network-based scores for each gene. To validate the scores, using Alzheimer's Disease Sequencing Project (ADSP) data, we constructed AD classifier models with Tree-based pipeline optimizer 2 (TPOT2), an automated machine learning framework. Models were optimized for performance, model complexity, and high aggregate network-based scores. Network-based scores successfully prioritized diverse feature sets-many not previously associated with AD-that are enriched in biologically meaningful body parts such as brain, and pathways including neuronal signaling, potassium channels, and creatine metabolism. The results suggested that knowledge graphs and network-informed embeddings can capture both known and novel insights into AD mechanisms. Additionally, integrating networkbased scores with feature-set-guided TPOT2 offers a scalable and biologically interpretable framework for AD drug repurposing and discovery.

Department(s)

Cooperative Engineering Program

Document Type

Article

DOI

10.1142/9789819824755_0059

Publication Date

1-1-2026

Recommended Citation

Obafemi-Ajayi, Tayo; Orlenko, Alena; Li, Binglan; Khanjani, Neda; Venkatesan, Mythreye; Shen, Li; Ritchie, Marylyn D.; Wang, Zhiping Paul; and Moore, Jason H., "A random-walk-based learning framework to uncover novel gene candidates for Alzheimer's disease therapy" (2026). Faculty Scholarship. 71.
https://bearworks.missouristate.edu/articles00/71

Journal Title

Pacific Symposium on Biocomputing Pacific Symposium on Biocomputing