In silico prediction of high-resolution Hi-C interaction matrices
Existing computational approaches to predict long-range regulatory interactions do not fully exploit high-resolution Hi-C datasets. Here the authors present a Random Forests regression-based approach to predict high-resolution Hi-C counts using one-dimensional regulatory genomic signals.
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2019-12-01
|
| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-019-13423-8 |