Evidence map›Paper›PMID 28815103›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2017

Search Datasets in Literature: A Case Study of GWAS.

Xiao Dong, Yaoyun Zhang, Hua Xu

Abstract read
PubMed Full text at PMC
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

3 authors.

Xiao DongSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Yaoyun ZhangSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Hua XuSchool of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

One of the missions of the NIH BD2K (Big Data to Knowledge) initiative is to make data discoverable and promote the re-use of existing datasets. Our ultimate goal is to develop a scalable approach that can automatically scan millions of scientific publications and identify underlying data sets. Using Genome-Wide Association Studies (GWAS) as a use case, we conducted an initial study to identify GWAS dataset attributes in MEDLINE abstracts, by developing a hybrid approach that combines domain dictionaries and pattern-based rules. The automatic GWAS dataset attribute recognition system achieved an F-measure of 84.85%. We further applied the GWAS attribute recognition system to indexing MEDLINE abstracts and built an online GWAS dataset search engine called "GWAS Dataset Finder". Our evaluation showed that the GWAS Dataset Finder outperformed PubMed significantly in retrieving literature with desired datasets. Our study demonstrates the potential application of text mining methods in building the data discovery index. It can create a better index of literature linked with their underlying data sets, thus improving data discoverability.

Identifiers

PMID28815103
PMCPMC5543360

What OpenQuestion holds

Textmetadata
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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.