SemanticCAP: Chromatin Accessibility Prediction Enhanced by Features Learning from a Language Model.

ABSTRACT: A large number of inorganic and organic compounds are able to bind DNA and form complexes, among which drug-related molecules are important. Chromatin accessibility changes not only directly affect drug-DNA interactions, but they can promote or inhibit the expression of the critical genes associated with drug resistance by affecting the DNA binding capacity of TFs and transcriptional regulators. However, the biological experimental techniques for measuring it are expensive and time-consuming. In recent years, several kinds of computational methods have been proposed to identify accessible regions of the genome. Existing computational models mostly ignore the contextual information provided by the bases in gene sequences. To address these issues, we proposed a new solution called SemanticCA

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC9028922 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature