Predicting gene expression divergence between single-copy orthologs in two species.

ABSTRACT: Predicting gene expression divergence is integral to understanding the emergence of new biological functions and associated traits. Whereas several sophisticated methods have been developed for this task, their applications are either limited to duplicate genes or require expression data from more than two species. Thus, here we present PiXi, the first machine learning framework for predicting gene expression divergence between single-copy orthologs in two species. PiXi models gene expression evolution as an Ornstein-Uhlenbeck process, and overlays this model with multi-layer neural network, random forest, and support vector machine architectures for making predictions. It outputs the predicted class "conserved" or "diverged" for each pair of orthologs, as well as their predicted expressio

SUBMITTER: Piya AA 

PROVIDER: S-EPMC10220509 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature