Reverse network diffusion to remove indirect noise for better inference of gene regulatory networks.

ABSTRACT:

Motivation

Gene regulatory networks (GRNs) are vital tools for delineating regulatory relationships between transcription factors and their target genes. The boom in computational biology and various biotechnologies has made inferring GRNs from multi-omics data a hot topic. However, when networks are constructed from gene expression data, they often suffer from false-positive problem due to the transitive effects of correlation. The presence of spurious noise edges obscures the real gene interactions, which makes downstream analyses, such as detecting gene function modules and predicting disease-related genes, difficult and inefficient. Therefore, there is an urgent and compelling need to develop network denoising methods to improve the accuracy of GRN inference.

Results

In

SUBMITTER: Yu J 

PROVIDER: S-EPMC11236096 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature