- author = "Alena Orlenko and Binglan Li and Neda Khanjani and Mythreye Venkatesan and Li Shen and Marylyn D. Ritchie and Zhiping Paul Wang and Tayo Obafemi-Ajayi and Jason H. Moore",
- title = "A random-walk-based learning framework to uncover novel gene candidates for Alzheimer's disease therapy",
- booktitle = "BIOCOMPUTING 2026, Pacific Symposium on Biocomputing",
- year = "2026",
- editor = "Russ B Altman and Lawrence Hunter and Marylyn D. Ritchie and Tiffany Murray and Teri E. Klein",
- pages = "815--829",
- address = "Kohala Coast, Hawaii, USA",
- month = jan # " 3-7",
- publisher = "World Scientific Publishing Co. Pte. Ltd",
- keywords = "genetic algorithms, genetic programming, TPOT, genomics; Alzheimer disease, random walks, knowledge graph, network analysis",
- ISSN = "2335-6928",
-
URL = "
https://psb.stanford.edu/psb-online/proceedings/psb26/orlenko.pdf",
-
URL = "
https://www.worldscientific.com/doi/epdf/10.1142/14631",
- size = "15 pages",
- abstract = "Identifying repurposable therapeutic targets for Alzheimer disease (AD) remains challenging due to various clinical and biological factors. we 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 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 Alzheimers 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 network-based scores with feature-set-guided TPOT2 offers a scalable and biologically interpretable framework for AD drug repurposing and discovery.",
-
notes = "https://psb.stanford.edu/previous/psb26/
Department of Computational Biomedicine Cedars-Sinai Medical Center, Los Angeles, CA, USA",
