Show simple item record

dc.contributor.authorValenzuela-Escárcega, Marco A
dc.contributor.authorBabur, Özgün
dc.contributor.authorHahn-Powell, Gus
dc.contributor.authorBell, Dane
dc.contributor.authorHicks, Thomas
dc.contributor.authorNoriega-Atala, Enrique
dc.contributor.authorWang, Xia
dc.contributor.authorSurdeanu, Mihai
dc.contributor.authorDemir, Emek
dc.contributor.authorMorrison, Clayton T
dc.date.accessioned2019-04-12T21:38:24Z
dc.date.available2019-04-12T21:38:24Z
dc.date.issued2018-09-26
dc.identifier.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/bay098en_US
dc.identifier.issn1758-0463
dc.identifier.pmid30256986
dc.identifier.doi10.1093/database/bay098
dc.identifier.urihttp://hdl.handle.net/10150/632060
dc.description.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.en_US
dc.description.sponsorshipDefense Advanced Research Projects Agency ( DARPA) Big Mechanism program [ARO W911NF-14-1-0395]en_US
dc.language.isoenen_US
dc.publisherOXFORD UNIV PRESSen_US
dc.relation.urlhttps://academic.oup.com/database/article/doi/10.1093/database/bay098/5107029en_US
dc.rights© The Author(s) 2018. Published by Oxford University Pressen_US
dc.titleLarge-scale automated machine reading discovers new cancer-driving mechanismsen_US
dc.typeArticleen_US
dc.contributor.departmentUniv Arizona, Dept Linguisten_US
dc.contributor.departmentUniv Arizona, Sch Informaten_US
dc.contributor.departmentUniv Arizona, Dept Mol & Cellular Biolen_US
dc.identifier.journalDATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATIONen_US
dc.description.noteOpen access journal.en_US
dc.description.collectioninformationThis 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 repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal published versionen_US
dc.source.journaltitleDatabase : the journal of biological databases and curation
refterms.dateFOA2019-04-12T21:38:25Z


Files in this item

Thumbnail
Name:
bay098.pdf
Size:
7.156Mb
Format:
PDF
Description:
Final Published version

This item appears in the following Collection(s)

Show simple item record