Improving gene regulatory network inference and assessment: The importance of using network structure.

ABSTRACT: Gene regulatory networks are graph models representing cellular transcription events. Networks are far from complete due to time and resource consumption for experimental validation and curation of the interactions. Previous assessments have shown the modest performance of the available network inference methods based on gene expression data. Here, we study several caveats on the inference of regulatory networks and methods assessment through the quality of the input data and gold standard, and the assessment approach with a focus on the global structure of the network. We used synthetic and biological data for the predictions and experimentally-validated biological networks as the gold standard (ground truth). Standard performance metrics and graph structural properties suggest that metho

SUBMITTER: Escorcia-Rodriguez JM 

PROVIDER: S-EPMC10012345 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature