ArticleNature communications2021
Co-evolution based machine-learning for predicting functional interactions between human genes.
Article in Nature communications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
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- CladeOScope-GSA: Revealing Evolutionary Associations Across Gene Sets.International journal of molecular sciences · 2026Article
- Profylo: A Python Package for Phylogenetic Profile Comparison and Analysis.Journal of molecular evolution · 2025Article
- Positive and unlabeled learning from hospital administrative data: a novel approach to identify sepsis cases.Health care management science · 2025Article
- Predicting Protein Function in the AI and Big Data Era.Biochemistry · 2025Review
- CCDC22 mutations that impair COMMD binding cause attenuated 3C/Ritscher-Schinzel syndrome.BMC medical genomics · 2025Article
- EvoWeaver: large-scale prediction of gene functional associations from coevolutionary signals.Nature communications · 2025Article
- Artificial intelligence for geoscience: Progress, challenges, and perspectives.Innovation (Cambridge (Mass.)) · 2024Review
- Using multi-scale genomics to associate poorly annotated genes with rare diseases.Genome medicine · 2024Article
- Mining salt stress-related genes inaBIOTECH · 2023Article
- ncOrtho: efficient and reliable identification of miRNA orthologs.Nucleic acids research · 2023Article
- Phylogenetic profiling in eukaryotes comes of age.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Identification of hidden associations among eukaryotic genes through statistical analysis of coevolutionary transitions.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Multi-omics data integration analysis identifies the spliceosome as a key regulator of DNA double-strand break repair.NAR cancer · 2022Article
- Machine learning approaches for biomolecular, biophysical, and biomaterials research.Biophysics reviews · 2022Review
- Machine-learning of complex evolutionary signals improves classification of SNVs.NAR genomics and bioinformatics · 2022Article
- Inverse Potts model improves accuracy of phylogenetic profiling.Bioinformatics (Oxford, England) · 2022Article
- Article
- Machine Learning Advances in Microbiology: A Review of Methods and Applications.Frontiers in microbiology · 2022Review
Corrections and comments
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Over the next decade, more than a million eukaryotic species are expected to be fully sequenced. This has the potential to improve our understanding of genotype and phenotype crosstalk, gene function and interactions, and answer evolutionary questions. Here, we develop a machine-learning approach for utilizing phylogenetic profiles across 1154 eukaryotic species. This method integrates co-evolution across eukaryotic clades to predict functional interactions between human genes and the context for these interactions. We benchmark our approach showing a 14% performance increase (auROC) compared to previous methods. Using this approach, we predict functional annotations for less studied genes. We focus on DNA repair and verify that 9 of the top 50 predicted genes have been identified elsewhere, with others previously prioritized by high-throughput screens. Overall, our approach enables better annotation of function and functional interactions and facilitates the understanding of evolutionary processes underlying co-evolution. The manuscript is accompanied by a webserver available at: https://mlpp.cs.huji.ac.il .
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