Benchmarking causal reasoning algorithms for gene expression-based compound mechanism of action analysis.

ABSTRACT:

Background

Elucidating compound mechanism of action (MoA) is beneficial to drug discovery, but in practice often represents a significant challenge. Causal Reasoning approaches aim to address this situation by inferring dysregulated signalling proteins using transcriptomics data and biological networks; however, a comprehensive benchmarking of such approaches has not yet been reported. Here we benchmarked four causal reasoning algorithms (SigNet, CausalR, CausalR ScanR and CARNIVAL) with four networks (the smaller Omnipath network vs. 3 larger MetaBase™ networks), using LINCS L1000 and CMap microarray data, and assessed to what extent each factor dictated the successful recovery of direct targets and compound-associated signalling pathways in a benchmark dataset comprising 269 comp

SUBMITTER: Hosseini-Gerami L 

PROVIDER: S-EPMC10111792 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature