Large-scale Automated Machine Reading Discovers New Cancer-driving Mechanisms

Overview

Journal Database (Oxford)

Specialty Biology

Date 2018 Sep 27

PMID 30256986

Citations 20

Authors

Marco A Valenzuela-Escarcega

Ozgun Babur

Gus Hahn-Powell

Dane Bell

Thomas Hicks

Enrique Noriega-Atala

Xia Wang

Mihai Surdeanu

Emek Demir

Clayton T Morrison

Affiliations

Soon will be listed here.

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.

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