Large-scale automated machine reading discovers new cancer-driving mechanisms
AuthorValenzuela-Escárcega, Marco A
Morrison, Clayton T
AffiliationUniv Arizona, Dept Linguist
Univ Arizona, Sch Informat
Univ Arizona, Dept Mol & Cellular Biol
MetadataShow full item record
PublisherOXFORD UNIV PRESS
CitationMarco A Valenzuela-Escárcega, Özgün Babur, Gus Hahn-Powell, Dane Bell, Thomas Hicks, Enrique Noriega-Atala, Xia Wang, Mihai Surdeanu, Emek Demir, Clayton T Morrison, Large-scale automated machine reading discovers new cancer-driving mechanisms, Database, Volume 2018, 2018, bay098, https://doi.org/10.1093/database/bay098
Rights© The Author(s) 2018. Published by Oxford University Press
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at email@example.com.
AbstractPubMed, 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.
NoteOpen access journal.
VersionFinal published version
SponsorsDefense Advanced Research Projects Agency ( DARPA) Big Mechanism program [ARO W911NF-14-1-0395]