ArticleBioinformatics (Oxford, England)2023
Joint embedding of biological networks for cross-species functional alignment.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 13 citations in OpenAlex.
- Computational strategies for cross-species knowledge transfer.Nature methods · 2026Review
- SPACE: STRING proteins as complementary embeddings.Bioinformatics (Oxford, England) · 2025Article
- Evolutionary divergence between homologous X-Y chromosome genes shapes sex-biased biology.Nature ecology & evolution · 2025Article
- A best-match approach for gene set analyses in embedding spaces.Genome research · 2024Article
- Computational strategies for cross-species knowledge transfer and translational biomedicine.ArXiv · 2024Article
- Joint representation of molecular networks from multiple species improves gene classification.PLoS computational biology · 2024Article
- SAMNA: accurate alignment of multiple biological networks based on simulated annealing.Journal of integrative bioinformatics · 2023Article
- Supervised biological network alignment with graph neural networks.Bioinformatics (Oxford, England) · 2023Article
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
motivationModel 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.
resultsWe 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. AVAILABILITY AND IMPLEMENTATION: https://github.com/ylaboratory/ETNA.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.