Inferring Differential Networks by Integrating Gene Expression Data With Additional Knowledge.

ABSTRACT: Evidences increasingly indicate the involvement of gene network rewiring in disease development and cell differentiation. With the accumulation of high-throughput gene expression data, it is now possible to infer the changes of gene networks between two different states or cell types via computational approaches. However, the distribution diversity of multi-platform gene expression data and the sparseness and high noise rate of single-cell RNA sequencing (scRNA-seq) data raise new challenges for existing differential network estimation methods. Furthermore, most existing methods are purely rely on gene expression data, and ignore the additional information provided by various existing biological knowledge. In this study, to address these challenges, we propose a general framework, named we

SUBMITTER: Liu C 

PROVIDER: S-EPMC8632038 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature