Benjamin M. Gyori - Machine-assisted curation using automated knowledge extraction and assembly
Channel: Biocuration Conference 2023
Date: 21/06/2023

*Title:* Machine-assisted curation of molecular mechanisms using automated knowledge extraction and assembly

*Session:* April 25 - Ai And Text Mining 2 - 02

*Abstract:* Curated resources such as protein-protein interaction databases and pathway databases require substantial human effort to maintain. An important bottleneck is the large body of published literature containing such information and the rate at which new publications appear. We present an approach that combines text mining and knowledge assembly to extend existing curated resources automatically using the INDRA system [1,2]. INDRA is an open-source Python library that integrates multiple text mining systems that can extract relations representing molecular mechanisms such as binding, phosphorylation and transcriptional regulation from publications at scale. Individual extractions providing evidence for each mechanism are then assembled from different text mining systems and publications. Assembly involves finding overlaps and redundancies in mechanisms extracted from published papers (using an ontology-guided approach) and using probability models to assess confidence and reduce machine reading errors. Beyond automated extraction, assembly and confidence assessment, INDRA also makes available a web-based interface to review assembled mechanisms along with supporting evidence and mark any errors to improve downstream usage. Automatically assembled knowledge extends and enriches curated resources in several ways: by (1) finding new relationships that have not yet been manually curated, (2) adding additional mechanistic detail to existing curated relationships and (3) finding additional evidence for existing curated relationships in new experimental contexts. We demonstrate all forms of extensions (1-3) using INDRA on human protein-protein interactions in BioGRID [3] and kinase-substrate annotations in PhosphoSitePlus [4]. We discuss characteristics of this machine-assisted curation workflow in terms of the number of assembled mechanisms that need to be reviewed to find correct new relationships (i.e., the curation yield) from text mining. Finally, we quantify the effect of these extensions on downstream data analysis [5]. Overall, this constitutes a reusable workflow for machine-assisted curation that can be applied to a broad range of resources to extend and enrich their content.

*References*:

[1] Gyori BM, Bachman JA, Subramanian K, Muhlich JL, Galescu L, Sorger PK. From word models to executable models of signaling networks using automated assembly.  Molecular Systems Biology, 2017 13(11):954. https://doi.org/10.15252/msb.20177651

[2] Bachman JA, Gyori BM, Sorger PK Automated assembly of molecular mechanisms at scale from text mining and curated databases bioRxiv, 2022. https://doi.org/10.1101/2022.08.30.505688

[3] Oughtred R, Rust J, Chang C, Breitkreutz BJ, Stark C, Willems A, Boucher L, Leung G, Kolas N, Zhang F, Dolma S, Coulombe-Huntington J, Chatr-Aryamontri A, Dolinski K, Tyers M. The BioGRID database: A comprehensive biomedical resource of curated protein, genetic, and chemical interactions. Protein Sci. 2021;30(1):187-200. https://doi.org/10.1002/pro.3978

[4] Hornbeck PV, Kornhauser JM, Latham V, Murray B, Nandhikonda V, Nord A, Skrzypek E, Wheeler T, Zhang B, Gnad F. 15 years of PhosphoSitePlus®: integrating post-translationally modified sites, disease variants and isoforms. Nucleic Acids Res. 2019;47(D1):D433-D441. https://doi.org/10.1093/nar/gky1159

[5] Bachman JA, Sorger PK, Gyori BM. Assembling a corpus of phosphoproteomic annotations using ProtMapper to normalize site information from databases and text mining bioRxiv, 2022. https://doi.org/10.1101/822668