Evidence map›Paper›PMID 30256986›Full record

ArticleDatabase : the journal of biological databases and curation2018

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

Abstract read
In one paragraph

Article in Database : the journal of biological databases and curation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

25 citing papers in PubMed.

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  6. Semantics-enabled biomedical literature analytics.Journal of biomedical informatics · 2024
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors.

Marco A Valenzuela-EscárcegaDepartment of Computer Science, University of Arizona, Tucson, AZ, USA.
Özgün BaburSchool of Medicine, Oregon Health & Science University, Portland, OR, USA.
Gus Hahn-PowellDepartment of Linguistics, University of Arizona, Tucson, AZ, USA.
Dane BellDepartment of Linguistics, University of Arizona, Tucson, AZ, USA.
Thomas HicksDepartment of Computer Science, University of Arizona, Tucson, AZ, USA.
Enrique Noriega-AtalaSchool of Information, University of Arizona, Tucson, AZ, USA.
Xia WangDepartment of Molecular and Cellular Biology, University of Arizona, Tucson, AZ, USA.
Mihai SurdeanuDepartment of Computer Science, University of Arizona, Tucson, AZ, USA.
Emek DemirSchool of Medicine, Oregon Health & Science University, Portland, OR, USA.
Clayton T MorrisonSchool of Information, University of Arizona, Tucson, AZ, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Machine LearningAlgorithmsAutomationHumansNeoplasmsPublications

Identifiers

PMID30256986
PMCPMC6156821

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.