Hyperbolic representation learning in multi-layer tissue networks

Predicting tissue-specific protein functions and proteinprotein interactions (PPI) is essential for understanding human biology, diseases, and potential therapeutics.Recently, as a promising direction, more and more complex unsupervised feature learning approaches have emerged in the field, but none of them consider the scale-free nature and the underlying geometry of multi-layer PPI networks.Therefore, this study proposes contextualized, tissue-specific representation learning in non-Euclidean geometries and demonstrates that hyperbolic embeddings capture the structure of multi-layer PPI networks with less distortion and achieve better performance in tissue-specific protein function prediction.