An intrinsically interpretable neural network architecture for sequence-to-function learning

Mostrar otras versiones (1)

MOTIVATION: Sequence-based deep learning approaches have been shown to predict a multitude of functional genomic readouts, including regions of open chromatin and RNA expression of genes. However, a major limitation of current methods is that model interpretation relies on computationally demanding...

Descripción completa

Detalles Bibliográficos
Autores principales: Balcı, Ali Tuğrul, Ebeid, Mark Maher, Benos, Panayiotis V, Kostka, Dennis, Chikina, Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2023
Materias:
Regulatory and Functional Genomics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311317/
https://www.ncbi.nlm.nih.gov/pubmed/37387140
http://dx.doi.org/10.1093/bioinformatics/btad271

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10311317/
https://www.ncbi.nlm.nih.gov/pubmed/37387140
http://dx.doi.org/10.1093/bioinformatics/btad271

Ejemplares similares