In silico prediction of high-resolution Hi-C interaction matrices

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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Detalles Bibliográficos
Autores principales: Shilu Zhang, Deborah Chasman, Sara Knaack, Sushmita Roy
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2019
Materias:
Science
Q
Acceso en línea:https://doaj.org/article/371269a5f21c4aab82d477791ec36389
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