Product Manifold Representations for Learning on Biological Pathways

• URL: http://arxiv.org/abs/2401.15478v1
• Date: Sat, 27 Jan 2024 18:46:19 GMT
• Title: Product Manifold Representations for Learning on Biological Pathways
• Authors: Daniel McNeela, Frederic Sala, Anthony Gitter
• Abstract summary: We investigate the effects of embedding pathway graphs in non-Euclidean mixed-curvature spaces. We train a supervised model using the learned node embeddings to predict missing protein-protein interactions in pathway graphs. We find large reductions in distortion and boosts on in-distribution edge prediction performance as a result of using mixed-curvature embeddings.
• Score: 13.0916239254532
• License: http://creativecommons.org/licenses/by/4.0/
• 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 model using the learned node embeddings to predict missing protein-protein interactions in pathway graphs. We find large reductions in distortion and boosts on in-distribution edge prediction performance as a result of using mixed-curvature embeddings and their corresponding graph neural network models. However, we find that mixed-curvature representations underperform existing baselines on out-of-distribution edge prediction performance suggesting that these representations may overfit to the training graph topology. We provide our mixed-curvature product GCN code at https://github.com/mcneela/Mixed-Curvature-GCN and our pathway analysis code at https://github.com/mcneela/Mixed-Curvature-Pathways.

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