Evidence map›Paper›PMID 25155502›Full record

ArticleBMC bioinformatics2014

Identification of highly related references about gene-disease association.

Rey-Long Liu, Chia-Chun Shih

Abstract read
In one paragraph

Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
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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

2 authors.

Rey-Long LiuDepartment of Medical Informatics, Tzu Chi University, Hualien, Taiwan. rlliutcu@mail.tcu.edu.tw.
Chia-Chun Shih

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCuration of gene-disease associations published in literature should be based on careful and frequent survey of the references that are highly related to specific gene-disease associations. Retrieval of the references is thus essential for timely and complete curation.

resultsWe present a technique CRFref (Conclusive, Rich, and Focused References) that, given a gene-disease pair < g, d>, ranks high those biomedical references that are likely to provide conclusive, rich, and focused results about g and d. Such references are expected to be highly related to the association between g and d. CRFref ranks candidate references based on their scores. To estimate the score of a reference r, CRFref estimates and integrates three measures: degree of conclusiveness, degree of richness, and degree of focus of r with respect to < g, d>. To evaluate CRFref, experiments are conducted on over one hundred thousand references for over one thousand gene-disease pairs. Experimental results show that CRFref performs significantly better than several typical types of baselines in ranking high those references that expert curators select to develop the summaries for specific gene-disease associations.

conclusionCRFref is a good technique to rank high those references that are highly related to specific gene-disease associations. It can be incorporated into existing search engines to prioritize biomedical references for curators and researchers, as well as those text mining systems that aim at the study of gene-disease associations.

Indexed as

Biomedical ResearchComputational BiologyData MiningDiseaseHumansPublicationsSearch Engine

Identifiers

PMID25155502
PMCPMC4162969

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LicenceCC BY
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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.