Product Manifold Representations for Learning on Biological Pathways.

ABSTRACT: Machine learning models that embed graphs in non-Euclidean spaces have shown substantial benefits in a variety of contexts, but their application has not been studied extensively in the biological domain, particularly with respect to biological pathway graphs. Such graphs exhibit a variety of complex network structures, presenting challenges to existing embedding approaches. Learning high-quality embeddings for biological pathway graphs is important for researchers looking to understand the underpinnings of disease and train high-quality predictive models on these networks. In this work, we investigate the effects of embedding pathway graphs in non-Euclidean mixed-curvature spaces and compare against traditional Euclidean graph representation learning models. We then train a supervised mod

SUBMITTER: McNeela D 

PROVIDER: S-EPMC11838783 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature