Graph embedding and unsupervised learning predict genomic sub-compartments from HiC chromatin interaction data
Accurate identification of sub-compartments from chromatin interaction data remains a challenge. Here, the authors introduce an algorithm combining graph embedding and unsupervised learning to predict sub-compartments using Hi-C data.
Main Authors: | Haitham Ashoor, Xiaowen Chen, Wojciech Rosikiewicz, Jiahui Wang, Albert Cheng, Ping Wang, Yijun Ruan, Sheng Li |
---|---|
Format: | Article |
Language: | English |
Published: |
Nature Publishing Group
2020-03-01
|
Series: | Nature Communications |
Online Access: | https://doi.org/10.1038/s41467-020-14974-x |
Similar Items
-
HiC-DC+ enables systematic 3D interaction calls and differential analysis for Hi-C and HiChIP
by: Merve Sahin, et al.
Published: (2021-06-01) -
Enhanced Unsupervised Graph Embedding via Hierarchical Graph Convolution Network
by: H. Zhang, et al.
Published: (2020-01-01) -
Comparative Analysis of Unsupervised Protein Similarity Prediction Based on Graph Embedding
by: Yuanyuan Zhang, et al.
Published: (2021-09-01) -
Hi–C interaction graph analysis reveals the impact of histone modifications in chromatin shape
by: Emre Sefer
Published: (2021-07-01) -
Exploring the Semantic Content of Unsupervised Graph Embeddings: An Empirical Study
by: Stephen Bonner, et al.
Published: (2019-06-01)