From expression footprints to causal pathways: contextualizing large signaling networks with CARNIVAL.

ABSTRACT: While gene expression profiling is commonly used to gain an overview of cellular processes, the identification of upstream processes that drive expression changes remains a challenge. To address this issue, we introduce CARNIVAL, a causal network contextualization tool which derives network architectures from gene expression footprints. CARNIVAL (CAusal Reasoning pipeline for Network identification using Integer VALue programming) integrates different sources of prior knowledge including signed and directed protein-protein interactions, transcription factor targets, and pathway signatures. The use of prior knowledge in CARNIVAL enables capturing a broad set of upstream cellular processes and regulators, leading to a higher accuracy when benchmarked against related tools. Implementation as

SUBMITTER: Liu A 

PROVIDER: S-EPMC6848167 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature