A Multi-Cohort and Multi-Omics Meta-Analysis Framework to Identify Network-Based Gene Signatures.

ABSTRACT: Although massive amounts of condition-specific molecular profiles are being accumulated in public repositories every day, meaningful interpretation of these data remains a major challenge. In an effort to identify the biomarkers that describe the key biological phenomena for a given condition, several approaches have been developed over the past few years. However, the majority of these approaches either (i) do not consider the known intermolecular interactions, or (ii) do not integrate molecular data of multiple types (e.g., genomics, transcriptomics, proteomics, epigenomics, etc.), and thus potentially fail to capture the true biological changes responsible for complex diseases (e.g., cancer). In addition, these approaches often ignore the heterogeneity and study bias present in independ

SUBMITTER: Shafi A 

PROVIDER: S-EPMC6434849 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature