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.
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| Main Authors: | , , , |
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| Format: | article |
| Language: | EN |
| Published: |
Nature Portfolio
2019
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| Subjects: | |
| Online Access: | https://doaj.org/article/371269a5f21c4aab82d477791ec36389 |
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