Network-based cancer genomic data integration for pattern discovery.

ABSTRACT:

Background

Since genes involved in the same biological modules usually present correlated expression profiles, lots of computational methods have been proposed to identify gene functional modules based on the expression profiles data. Recently, Sparse Singular Value Decomposition (SSVD) method has been proposed to bicluster gene expression data to identify gene modules. However, this model can only handle the gene expression data where no gene interaction information is integrated. Ignoring the prior gene interaction information may produce the identified gene modules hard to be biologically interpreted.

Results

In this paper, we develop a Sparse Network-regularized SVD (SNSVD) method that integrates a prior gene interaction network from a protein protein interaction network

SUBMITTER: Zhu F 

PROVIDER: S-EPMC8662848 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature