Causal network analysis of omics data using prior knowledge databases.

ABSTRACT: Identifying causal relationships in omics data is essential for understanding underlying biological processes. However, detecting these relationships remains challenging due to the complexity of molecular networks and observational data limitations. To guide researchers, we conducted a systematic literature review of data-driven causal omics analysis methods that use structured prior knowledge from regulatory and interaction databases. We grouped methods into three approaches based on the extent of prior knowledge integration: regulon-level (direct regulator-target links, straightforward interpretation, but with the risk of oversimplification), flow-level (multi-step propagation from regulators to targets, broader mechanism explanation, but lacking uncertainty modeling), and network-level

SUBMITTER: Svinin G 

PROVIDER: S-EPMC12703490 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature