ArticleBioinformatics (Oxford, England)2016
DeepMeSH: deep semantic representation for improving large-scale MeSH indexing.
Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Enhancing automated indexing of publication types and study designs in biomedical literature using full-text features.medRxiv : the preprint server for health sciences · 2025Article
- Context-Aware Contrastive Representation Learning for Zero-Shot Biomedical Text Classification.Proceedings. IEEE International Conference on Bioinformatics and Biomedicine · 2024Article
- Is metadata of articles about COVID-19 enough for multilabel topic classification task?Database : the journal of biological databases and curation · 2024Article
- Chemical identification and indexing in PubMed full-text articles using deep learning and heuristics.Database : the journal of biological databases and curation · 2022Article
- Multi-probe attention neural network for COVID-19 semantic indexing.BMC bioinformatics · 2022Article
- Identification of Drug-Disease Associations Using a Random Walk with Restart Method and Supervised Learning.Computational and mathematical methods in medicine · 2022Article
- Thesaurus-based word embeddings for automated biomedical literature classification.Neural computing & applications · 2022Article
- Recent advances in biomedical literature mining.Briefings in bioinformatics · 2021Review
- COS: A new MeSH term embedding incorporating corpus, ontology, and semantic predications.PloS one · 2021Article
- Tackling Research Inefficiency in Degenerative Cervical Myelopathy: Illustrative Review.JMIR research protocols · 2020Article
- FullMeSH: improving large-scale MeSH indexing with full text.Bioinformatics (Oxford, England) · 2020Article
- Automatic MeSH Indexing: Revisiting the Subheading Attachment Problem.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Combining Contextualized Embeddings and Prior Knowledge for Clinical Named Entity Recognition: Evaluation Study.JMIR medical informatics · 2019Article
- ML-Net: multi-label classification of biomedical texts with deep neural networks.Journal of the American Medical Informatics Association : JAMIA · 2019Article
- NetGO: improving large-scale protein function prediction with massive network information.Nucleic acids research · 2019Article
- A High Recall Classifier for Selecting Articles for MEDLINE Indexing.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Biomedical semantic indexing by deep neural network with multi-task learning.BMC bioinformatics · 2018Article
- Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature.Journal of biomedical semantics · 2018Article
- A probabilistic automated tagger to identify human-related publications.Database : the journal of biological databases and curation · 2018Article
- Baseline and extensions approach to information retrieval of complex medical data: Poznan's approach to the bioCADDIE 2016.Database : the journal of biological databases and curation · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
motivationMedical 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.
methodsWe 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.
resultsDeepMeSH 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.
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Registered trials
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.