Cargando…
Reconstructing high-resolution chromosome three-dimensional structures by Hi-C complex networks
BACKGROUND: Hi-C data have been widely used to reconstruct chromosomal three-dimensional (3D) structures. One of the key limitations of Hi-C is the unclear relationship between spatial distance and the number of Hi-C contacts. Many methods used a fixed parameter when converting the number of Hi-C co...
Autores principales: | Liu, Tong, Wang, Zheng |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6309071/ https://www.ncbi.nlm.nih.gov/pubmed/30591009 http://dx.doi.org/10.1186/s12859-018-2464-z |
Ejemplares similares
-
Chromosome3D: reconstructing three-dimensional chromosomal structures from Hi-C interaction frequency data using distance geometry simulated annealing
por: Adhikari, Badri, et al.
Publicado: (2016) -
Bayesian Estimation of Three-Dimensional Chromosomal Structure from Single-Cell Hi-C Data
por: Rosenthal, Michael, et al.
Publicado: (2019) -
HiCNN2: Enhancing the Resolution of Hi-C Data Using an Ensemble of Convolutional Neural Networks
por: Liu, Tong, et al.
Publicado: (2019) -
HiCNN: a very deep convolutional neural network to better enhance the resolution of Hi-C data
por: Liu, Tong, et al.
Publicado: (2019) -
Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus
por: Zhang, Yan, et al.
Publicado: (2018)