Cargando…
DECODE: a Deep-learning framework for Condensing enhancers and refining boundaries with large-scale functional assays
MOTIVATION: Mapping distal regulatory elements, such as enhancers, is a cornerstone for elucidating how genetic variations may influence diseases. Previous enhancer-prediction methods have used either unsupervised approaches or supervised methods with limited training data. Moreover, past approaches...
Autores principales: | Chen, Zhanlin, Zhang, Jing, Liu, Jason, Dai, Yi, Lee, Donghoon, Min, Martin Renqiang, Xu, Min, Gerstein, Mark |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8275369/ https://www.ncbi.nlm.nih.gov/pubmed/34252960 http://dx.doi.org/10.1093/bioinformatics/btab283 |
Ejemplares similares
-
CROTON: an automated and variant-aware deep learning framework for predicting CRISPR/Cas9 editing outcomes
por: Li, Victoria R, et al.
Publicado: (2021) -
JEDI: circular RNA prediction based on junction encoders and deep interaction among splice sites
por: Jiang, Jyun-Yu, et al.
Publicado: (2021) -
Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale
por: Mendelevich, Asia, et al.
Publicado: (2023) -
stPlus: a reference-based method for the accurate enhancement of spatial transcriptomics
por: Shengquan, Chen, et al.
Publicado: (2021) -
Resolving diverse protein–DNA footprints from exonuclease-based ChIP experiments
por: Biswas, Anushua, et al.
Publicado: (2021)