Detecting Interspecific Positive Selection Using Convolutional Neural Networks.

ABSTRACT: Traditional statistical methods using maximum likelihood and Bayesian inference can detect positive selection from an interspecific phylogeny and a codon sequence alignment based on model assumptions, but they are prone to false positives due to alignment errors and can lack power. These problems are particularly pronounced when faced with high levels of indels and divergence. To address these issues, we trained and tested convolutional neural network models on simulated data and achieved higher accuracy in detecting selection across a specific range of phylogenetic scenarios and evolutionary modes. This advantage is particularly evident when performing inference on noisy data prone to misalignments. Our method shows some ability to account for these errors, where most statistical framewor

SUBMITTER: West C 

PROVIDER: S-EPMC12287699 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature