Predicting gene regulatory interactions based on spatial gene expression data and deep learning.

ABSTRACT: Reverse engineering of gene regulatory networks (GRNs) is a central task in systems biology. Most of the existing methods for GRN inference rely on gene co-expression analysis or TF-target binding information, where the determination of co-expression is often unreliable merely based on gene expression levels, and the TF-target binding data from high-throughput experiments may be noisy, leading to a high ratio of false links and missed links, especially for large-scale networks. In recent years, the microscopy images recording spatial gene expression have become a new resource in GRN reconstruction, as the spatial and temporal expression patterns contain much abundant gene interaction information. Till now, the spatial expression resources have been largely underexploited, and only a few tr

SUBMITTER: Yang Y 

PROVIDER: S-EPMC6764701 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature