Reverse causal reasoning: applying qualitative causal knowledge to the interpretation of high-throughput data.

ABSTRACT:

Background

Gene expression profiling and other genome-scale measurement technologies provide comprehensive information about molecular changes resulting from a chemical or genetic perturbation, or disease state. A critical challenge is the development of methods to interpret these large-scale data sets to identify specific biological mechanisms that can provide experimentally verifiable hypotheses and lead to the understanding of disease and drug action.

Results

We present a detailed description of Reverse Causal Reasoning (RCR), a reverse engineering methodology to infer mechanistic hypotheses from molecular profiling data. This methodology requires prior knowledge in the form of small networks that causally link a key upstream controller node representing a biological mech

SUBMITTER: Catlett NL 

PROVIDER: S-EPMC4222496 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature