PT  - JOURNAL ARTICLE
AU  - Pazos Obregón, Flavio
AU  - Silvera, Diego
AU  - Soto, Pablo
AU  - Yankilevich, Patricio
AU  - Guerberoff, Gustavo
AU  - Cantera, Rafael
TI  - Gene function prediction in five model eukaryotes based on gene relative location through machine learning
AID  - 10.1101/2021.08.27.457944
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.08.27.457944
4099  - http://biorxiv.org/content/early/2021/10/15/2021.08.27.457944.short
4100  - http://biorxiv.org/content/early/2021/10/15/2021.08.27.457944.full
AB  - Motiviation The function of most genes is unknown. The best results in gene function prediction are obtained with machine learning-based methods that combine multiple data sources, typically sequence derived features, protein structure and interaction data. Even though there is ample evidence showing that a gene’s function is not independent of its location, the few available examples of gene function prediction based on gene location relay on sequence identity between genes of different organisms and are thus subjected to the limitations of the relationship between sequence and function.Results Here we predict thousands of gene functions in five eukaryotes (Saccharomyces cerevisiae, Caenorhabditis elegans, Drosophila melanogaster, Mus musculus and Homo sapiens) using machine learning models trained with features derived from the location of genes in the genomes to which they belong. To the best of our knowledge this is the first work in which gene function prediction is successfully achieved in eukaryotic genomes using predictive features derived exclusively from the relative location of the genes.Contact fpazos{at}iibce.edu.uySupplementary information http://gfpml.bnd.edu.uyCompeting Interest StatementThe authors have declared no competing interest.