Cargando…
Characterizing chromatin folding coordinate and landscape with deep learning
Genome organization is critical for setting up the spatial environment of gene transcription, and substantial progress has been made towards its high-resolution characterization. The underlying molecular mechanism for its establishment is much less understood. We applied a deep-learning approach, va...
Autores principales: | Xie, Wen Jun, Qi, Yifeng, Zhang, Bin |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7544120/ https://www.ncbi.nlm.nih.gov/pubmed/32986691 http://dx.doi.org/10.1371/journal.pcbi.1008262 |
Ejemplares similares
-
Characterization of hundreds of regulatory landscapes in developing limbs reveals two regimes of chromatin folding
por: Andrey, Guillaume, et al.
Publicado: (2017) -
Mapping the glycosyltransferase fold landscape using interpretable deep learning
por: Taujale, Rahil, et al.
Publicado: (2021) -
DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes
por: Wang, Siguo, et al.
Publicado: (2022) -
HMGB1 coordinates SASP‐related chromatin folding and RNA homeostasis on the path to senescence
por: Sofiadis, Konstantinos, et al.
Publicado: (2021) -
Structural Modeling of Chromatin Integrates Genome Features and Reveals Chromosome Folding Principle
por: Xie, Wen Jun, et al.
Publicado: (2017)