Co-evolution based machine-learning for predicting functional interactions between human genes

Doron Stupp, Elad Sharon, Idit Bloch, Marinka Zitnik, Or Zuk*, Yuval Tabach*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Scopus citations


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:

Original languageAmerican English
Article number6454
JournalNature Communications
Issue number1
StatePublished - 9 Nov 2021

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© 2021, The Author(s).


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