Correlation set analysis: detecting active regulators in disease populations using prior causal knowledge.

ABSTRACT:

Background

Identification of active causal regulators is a crucial problem in understanding mechanism of diseases or finding drug targets. Methods that infer causal regulators directly from primary data have been proposed and successfully validated in some cases. These methods necessarily require very large sample sizes or a mix of different data types. Recent studies have shown that prior biological knowledge can successfully boost a method's ability to find regulators.

Results

We present a simple data-driven method, Correlation Set Analysis (CSA), for comprehensively detecting active regulators in disease populations by integrating co-expression analysis and a specific type of literature-derived causal relationships. Instead of investigating the co-expression level between

SUBMITTER: Huang CL 

PROVIDER: S-EPMC3382432 | biostudies-literature | 2012 Mar

REPOSITORIES: biostudies-literature