Improving the performance of supervised deep learning for regulatory genomics using phylogenetic augmentation.

ABSTRACT:

Motivation

Supervised deep learning is used to model the complex relationship between genomic sequence and regulatory function. Understanding how these models make predictions can provide biological insight into regulatory functions. Given the complexity of the sequence to regulatory function mapping (the cis-regulatory code), it has been suggested that the genome contains insufficient sequence variation to train models with suitable complexity. Data augmentation is a widely used approach to increase the data variation available for model training, however current data augmentation methods for genomic sequence data are limited.

Results

Inspired by the success of comparative genomics, we show that augmenting genomic sequences with evolutionarily related sequences from other s

SUBMITTER: Duncan AG 

PROVIDER: S-EPMC11042905 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature