DeepFGRN: inference of gene regulatory network with regulation type based on directed graph embedding.

ABSTRACT: The inference of gene regulatory networks (GRNs) from gene expression profiles has been a key issue in systems biology, prompting many researchers to develop diverse computational methods. However, most of these methods do not reconstruct directed GRNs with regulatory types because of the lack of benchmark datasets or defects in the computational methods. Here, we collect benchmark datasets and propose a deep learning-based model, DeepFGRN, for reconstructing fine gene regulatory networks (FGRNs) with both regulation types and directions. In addition, the GRNs of real species are always large graphs with direction and high sparsity, which impede the advancement of GRN inference. Therefore, DeepFGRN builds a node bidirectional representation module to capture the directed graph embedding re

SUBMITTER: Gao Z 

PROVIDER: S-EPMC10998536 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature