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Bioinformatics. 2016 Jun 15;32(12):i70-i79. doi: 10.1093/bioinformatics/btw294.

DeepMeSH: deep semantic representation for improving large-scale MeSH indexing.

Author information

1
School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China.
2
Department of Computer Science, University of Virginia, Charlottesville 22904-4740, USA.
3
Department of Computer Science, University of Illinois at Urbana-Champaign, IL 61801, USA.
4
Bioinformatics Center, Kyoto University, Institute for Chemical Research, Uji 611-0011, Japan Department of Computer Science, Aalto University, Finland.
5
School of Computer Science and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai 200433, China Centre for Computational System Biology, Fudan University, Shanghai 200433, China.

Abstract

MOTIVATION:

Medical Subject Headings (MeSH) indexing, which is to assign a set of MeSH main headings to citations, is crucial for many important tasks in biomedical text mining and information retrieval. Large-scale MeSH indexing has two challenging aspects: the citation side and MeSH side. For the citation side, all existing methods, including Medical Text Indexer (MTI) by National Library of Medicine and the state-of-the-art method, MeSHLabeler, deal with text by bag-of-words, which cannot capture semantic and context-dependent information well.

METHODS:

We propose DeepMeSH that incorporates deep semantic information for large-scale MeSH indexing. It addresses the two challenges in both citation and MeSH sides. The citation side challenge is solved by a new deep semantic representation, D2V-TFIDF, which concatenates both sparse and dense semantic representations. The MeSH side challenge is solved by using the 'learning to rank' framework of MeSHLabeler, which integrates various types of evidence generated from the new semantic representation.

RESULTS:

DeepMeSH achieved a Micro F-measure of 0.6323, 2% higher than 0.6218 of MeSHLabeler and 12% higher than 0.5637 of MTI, for BioASQ3 challenge data with 6000 citations.

AVAILABILITY AND IMPLEMENTATION:

The software is available upon request.

CONTACT:

zhusf@fudan.edu.cn

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
27307646
PMCID:
PMC4908368
DOI:
10.1093/bioinformatics/btw294
[Indexed for MEDLINE]
Free PMC Article

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