Deep Learning on Chromatin Accessibility.
Methods Mol Biol
; 2611: 325-333, 2023.
Article
em En
| MEDLINE
| ID: mdl-36807077
ABSTRACT
DNA accessibility has been a powerful tool in locating active regulatory elements in a cell type, but dissecting the combinatorial logic within these regulatory elements has been a continued challenge in the field. Deep learning models have been shown to be highly predictive models of regulatory DNA and have led to new biological insights on regulatory syntax and logic. Here, we provide a framework for deep learning in genomics that implements best practices and focuses on ease of use, versatility, and compatibility with existing tools for inference on DNA sequence.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Cromatina
/
Aprendizado Profundo
Tipo de estudo:
Guideline
/
Prognostic_studies
Idioma:
En
Ano de publicação:
2023
Tipo de documento:
Article