Predicting natural variation in the yeast phenotypic landscape with machine learning

ABSTRACT: Most organismal traits result from the complex interplay of many genetic and environmental factors, making their prediction difficult. Here, we used machine learning (ML) models to explore phenotype predictions for 223 traits measured across 1011 genome-sequenced Saccharomyces cerevisiae strains isolated world-wide. We benchmarked a ML pipeline with multiple linear and non-linear models to predict phenotypes from genotypes and gene expression and determined gradient boosting machines as best performing model. Gene function disruption scores and gene presence/absence emerged as best predictors, suggesting a considerable contribution of the accessory genome in controlling phenotypes. The prediction accuracy broadly varied among phenotypes, with stress resistance being easier to predict compa

SUBMITTER: Sakshi Khaiwal 

PROVIDER: S-SCDT-10_1038-S44320-025-00136-Y | biostudies-other |

REPOSITORIES: biostudies-other