Integrating Long-Range Regulatory Interactions to Predict Gene Expression Using Graph Convolutional Networks.

ABSTRACT: Long-range regulatory interactions among genomic regions are critical for controlling gene expression, and their disruption has been associated with a host of diseases. However, when modeling the effects of regulatory factors, most deep learning models either neglect long-range interactions or fail to capture the inherent 3D structure of the underlying genomic organization. To address these limitations, we present a Graph Convolutional Model for Epigenetic Regulation of Gene Expression (GC-MERGE). Using a graph-based framework, the model incorporates important information about long-range interactions via a natural encoding of genomic spatial interactions into the graph representation. It integrates measurements of both the global genomic organization and the local regulatory factors, s

SUBMITTER: Bigness J 

PROVIDER: S-EPMC9125570 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature