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

Autor: Bachman JA; Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA., Gyori BM; Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA., Sorger PK; Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA.; Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
Jazyk: angličtina
Zdroj: Molecular systems biology [Mol Syst Biol] 2023 May 09; Vol. 19 (5), pp. e11325. Date of Electronic Publication: 2023 Mar 20.
DOI: 10.15252/msb.202211325
Abstrakt: 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 Reasoning Assembler (INDRA). INDRA identifies full and partial overlaps in information extracted from published papers and pathway databases, uses predictive models to improve the reliability of machine reading, and thereby assembles individual pieces of information into non-redundant and broadly usable mechanistic knowledge. Using INDRA to create high-quality corpora of causal knowledge we show it is possible to extend protein-protein interaction databases and explain co-dependencies in the Cancer Dependency Map.
(© 2023 The Authors. Published under the terms of the CC BY 4.0 license.)
Databáze: MEDLINE
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