Machine learning accurately predicts the multivariate performance phenotype from morphology in lizards.

ABSTRACT: Completing the genotype-to-phenotype map requires rigorous measurement of the entire multivariate organismal phenotype. However, phenotyping on a large scale is not feasible for many kinds of traits, resulting in missing data that can also cause problems for comparative analyses and the assessment of evolutionary trends across species. Measuring the multivariate performance phenotype is especially logistically challenging, and our ability to predict several performance traits from a given morphology is consequently poor. We developed a machine learning model to accurately estimate multivariate performance data from morphology alone by training it on a dataset containing performance and morphology data from 68 lizard species. Our final, stacked model predicts missing performance data accura

SUBMITTER: Lailvaux SP 

PROVIDER: S-EPMC8782310 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature