Semi-supervised learning improves regulatory sequence prediction with unlabeled sequences.

ABSTRACT:

Motivation

Genome-wide association studies have systematically identified thousands of single nucleotide polymorphisms (SNPs) associated with complex genetic diseases. However, the majority of those SNPs were found in non-coding genomic regions, preventing the understanding of the underlying causal mechanism. Predicting molecular processes based on the DNA sequence represents a promising approach to understand the role of those non-coding SNPs. Over the past years, deep learning was successfully applied to regulatory sequence prediction using supervised learning. Supervised learning required DNA sequences associated with functional data for training, whose amount is strongly limited by the finite size of the human genome. Conversely, the amount of mammalian DNA sequences is exponen

SUBMITTER: Mourad R 

PROVIDER: S-EPMC10163727 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature