Unsupervised discovery of phenotype-specific multi-omics networks.

ABSTRACT:

Motivation

Complex diseases often involve a wide spectrum of phenotypic traits. Better understanding of the biological mechanisms relevant to each trait promotes understanding of the etiology of the disease and the potential for targeted and effective treatment plans. There have been many efforts towards omics data integration and network reconstruction, but limited work has examined the incorporation of relevant (quantitative) phenotypic traits.

Results

We propose a novel technique, sparse multiple canonical correlation network analysis (SmCCNet), for integrating multiple omics data types along with a quantitative phenotype of interest, and for constructing multi-omics networks that are specific to the phenotype. As a case study, we focus on miRNA-mRNA networks. Through sim

SUBMITTER: Shi WJ 

PROVIDER: S-EPMC6931269 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature