Evidence map›Paper›PMID 26072501›Full record

ArticleBioinformatics (Oxford, England)2015

MeSHLabeler: improving the accuracy of large-scale MeSH indexing by integrating diverse evidence.

Ke Liu, Shengwen Peng, Junqiu Wu, Chengxiang Zhai, Hiroshi Mamitsuka, Shanfeng Zhu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

  1. Article
  2. Article
  3. LitCovid ensemble learning for COVID-19 multi-label classification.Database : the journal of biological databases and curation · 2022
    Article
  4. Article
  5. Article
  6. Recent advances in biomedical literature mining.Briefings in bioinformatics · 2021
    Review
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. A probabilistic automated tagger to identify human-related publications.Database : the journal of biological databases and curation · 2018
    Article
  13. Article
  14. Article
  15. 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

6 authors.

Ke LiuSchool of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China.
Shengwen PengSchool of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China.
Junqiu WuSchool of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China.
Chengxiang ZhaiSchool of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China.
Hiroshi MamitsukaSchool of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China.
Shanfeng ZhuSchool of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China, School of Information Science & Engineering, Central South University, Changsha 410083, China, Department of Computer Science, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA, Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji 611-0011, Japan and Center for Computational System Biology, Fudan University, Shanghai 200433, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationMedical Subject Headings (MeSHs) are used by National Library of Medicine (NLM) to index almost all citations in MEDLINE, which greatly facilitates the applications of biomedical information retrieval and text mining. To reduce the time and financial cost of manual annotation, NLM has developed a software package, Medical Text Indexer (MTI), for assisting MeSH annotation, which uses k-nearest neighbors (KNN), pattern matching and indexing rules. Other types of information, such as prediction by MeSH classifiers (trained separately), can also be used for automatic MeSH annotation. However, existing methods cannot effectively integrate multiple evidence for MeSH annotation.

methodsWe propose a novel framework, MeSHLabeler, to integrate multiple evidence for accurate MeSH annotation by using 'learning to rank'. Evidence includes numerous predictions from MeSH classifiers, KNN, pattern matching, MTI and the correlation between different MeSH terms, etc. Each MeSH classifier is trained independently, and thus prediction scores from different classifiers are incomparable. To address this issue, we have developed an effective score normalization procedure to improve the prediction accuracy.

resultsMeSHLabeler won the first place in Task 2A of 2014 BioASQ challenge, achieving the Micro F-measure of 0.6248 for 9,040 citations provided by the BioASQ challenge. Note that this accuracy is around 9.15% higher than 0.5724, obtained by MTI. AVAILABILITY AND IMPLEMENTATION: The software is available upon request.

Indexed as

Medical Subject HeadingsSoftwareAbstracting and IndexingAlgorithmsData MiningMEDLINEReproducibility of Results

Identifiers

PMID26072501
PMCPMC4765864

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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.