Large-scale automated machine reading discovers new cancer-driving mechanisms

Marco A. Valenzuela-Escárcega, Özgün Babur, Gus Hahn-Powell, Dane Bell, Thomas Hicks, Enrique Noriega-Atala, Xia Wang, Mihai Surdeanu, Emek Demir, Clayton T. Morrison

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

PubMed, a repository and search engine for biomedical literature, now indexes >1 million articles each year. This exceeds the processing capacity of human domain experts, limiting our ability to truly understand many diseases. We present Reach, a system for automated, large-scale machine reading of biomedical papers that can extract mechanistic descriptions of biological processes with relatively high precision at high throughput. We demonstrate that combining the extracted pathway fragments with existing biological data analysis algorithms that rely on curated models helps identify and explain a large number of previously unidentified mutually exclusive altered signaling pathways in seven different cancer types. This work shows that combining human-curated 'big mechanisms' with extracted 'big data' can lead to a causal, predictive understanding of cellular processes and unlock important downstream applications.

Original languageEnglish (US)
JournalDatabase
Volume2018
Issue number2018
DOIs
StatePublished - Jan 1 2018

ASJC Scopus subject areas

  • Information Systems
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

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    Valenzuela-Escárcega, M. A., Babur, Ö., Hahn-Powell, G., Bell, D., Hicks, T., Noriega-Atala, E., Wang, X., Surdeanu, M., Demir, E., & Morrison, C. T. (2018). Large-scale automated machine reading discovers new cancer-driving mechanisms. Database, 2018(2018). https://doi.org/10.1093/database/bay098