Li, LechuanDannenfelser, RuthZhu, YuHejduk, NathanielSegarra, SantiagoYao, Vicky2024-05-082024-05-082023Li, L., Dannenfelser, R., Zhu, Y., Hejduk, N., Segarra, S., & Yao, V. (2023). Joint embedding of biological networks for cross-species functional alignment. Bioinformatics, 39(9), btad529. https://doi.org/10.1093/bioinformatics/btad529https://hdl.handle.net/1911/115694Model organisms are widely used to better understand the molecular causes of human disease. While sequence similarity greatly aids this cross-species transfer, sequence similarity does not imply functional similarity, and thus, several current approaches incorporate protein–protein interactions to help map findings between species. Existing transfer methods either formulate the alignment problem as a matching problem which pits network features against known orthology, or more recently, as a joint embedding problem.We propose a novel state-of-the-art joint embedding solution: Embeddings to Network Alignment (ETNA). ETNA generates individual network embeddings based on network topological structure and then uses a Natural Language Processing-inspired cross-training approach to align the two embeddings using sequence-based orthologs. The final embedding preserves both within and between species gene functional relationships, and we demonstrate that it captures both pairwise and group functional relevance. In addition, ETNA’s embeddings can be used to transfer genetic interactions across species and identify phenotypic alignments, laying the groundwork for potential opportunities for drug repurposing and translational studies.https://github.com/ylaboratory/ETNAengExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license. Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.Journal articlebtad529https://doi.org/10.1093/bioinformatics/btad529