Work

Hyperbolic representation learning in multi-layer tissue networks

Year: 2025

Type: conference-paper

Abstract: 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.

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-21

Authors: Domonkos Pogány, Péter Antal

Institution: Budapest University of Technology and Economics

Language: English

Topic: Bioinformatics and Genomic Networks

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: bronze