Automated assembly of molecular mechanisms at scale from text mining and curated databases.

ABSTRACT: The analysis of omic data depends on machine-readable information about protein interactions, modifications, and activities as found in protein interaction networks, databases of post-translational modifications, and curated models of gene and protein function. These resources typically depend heavily on human curation. Natural language processing systems that read the primary literature have the potential to substantially extend knowledge resources while reducing the burden on human curators. However, machine-reading systems are limited by high error rates and commonly generate fragmentary and redundant information. Here, we describe an approach to precisely assemble molecular mechanisms at scale using multiple natural language processing systems and the Integrated Network and Dynamical R

SUBMITTER: Bachman JA 

PROVIDER: S-EPMC10167483 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature