A supervised learning framework for chromatin loop detection in genome-wide contact maps
Abstract
Accurately predicting chromatin loops from genome-wide interaction matrices such as Hi-C data is critical to deepening our understanding of proper gene regulation. Current approaches are mainly focused on searching for statistically enriched dots on a genome-wide map. However, given the availability of orthogonal data types such as ChIA-PET, HiChIP, Capture Hi-C, and high-throughput imaging, a supervised learning approach could facilitate the...
Paper Details
Title
A supervised learning framework for chromatin loop detection in genome-wide contact maps
Published Date
Jul 9, 2020
Journal
Volume
11
Issue
1
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