Learning Retention Mechanisms and Evolutionary Parameters of Duplicate Genes from Their Expression Data.

ABSTRACT: Learning about the roles that duplicate genes play in the origins of novel phenotypes requires an understanding of how their functions evolve. A previous method for achieving this goal, CDROM, employs gene expression distances as proxies for functional divergence and then classifies the evolutionary mechanisms retaining duplicate genes from comparisons of these distances in a decision tree framework. However, CDROM does not account for stochastic shifts in gene expression or leverage advances in contemporary statistical learning for performing classification, nor is it capable of predicting the parameters driving duplicate gene evolution. Thus, here we develop CLOUD, a multi-layer neural network built on a model of gene expression evolution that can both classify duplicate gene retention m

SUBMITTER: DeGiorgio M 

PROVIDER: S-EPMC7947822 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature