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

Doron Stupp1, Elad Sharon1, Idit Bloch1

  • 1Department of Developmental Biology and Cancer Research, The Institute for Medical Research Israel-Canada, The Hebrew University of Jerusalem, 9112001, Jerusalem, Israel.

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|November 10, 2021
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Summary
This summary is machine-generated.

A new machine-learning method uses phylogenetic profiles to predict gene function and interactions across eukaryotic species. This approach enhances functional annotation and aids in understanding evolutionary co-evolution, with applications in DNA repair research.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Evolutionary Biology

Background:

  • Massive sequencing of eukaryotic genomes is imminent, offering opportunities to study gene function, genotype-phenotype relationships, and evolution.
  • Understanding gene function and interactions is crucial for biological research and disease understanding.

Purpose of the Study:

  • To develop a machine-learning approach for predicting functional gene interactions using phylogenetic profiles.
  • To enhance the annotation of gene function and interactions across eukaryotic species.
  • To investigate the evolutionary processes underlying co-evolution.

Main Methods:

  • Developed a machine-learning model utilizing phylogenetic profiles from 1154 eukaryotic species.
  • Integrated co-evolutionary signals across eukaryotic clades to predict gene interactions.
  • Benchmarked the approach against existing methods, demonstrating a 14% improvement in auROC.

Main Results:

  • Successfully predicted functional annotations for understudied genes.
  • Validated predictions in the DNA repair pathway, with 9 of the top 50 predicted genes confirmed and others prioritized by high-throughput screens.
  • The method shows significant performance improvement over previous techniques.

Conclusions:

  • The developed machine-learning approach improves functional gene annotation and interaction prediction.
  • Facilitates a deeper understanding of the evolutionary basis of co-evolution.
  • Provides a valuable tool for genomic and evolutionary research, with a webserver available for use.