{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T14:06:00Z","timestamp":1767276360451,"version":"3.37.3"},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"19","license":[{"start":{"date-parts":[[2019,3,9]],"date-time":"2019-03-09T00:00:00Z","timestamp":1552089600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","award":["IIS-1514204"],"award-info":[{"award-number":["IIS-1514204"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>MEDLINE is the primary bibliographic database maintained by National Library of Medicine (NLM). MEDLINE citations are indexed with Medical Subject Headings (MeSH), which is a controlled vocabulary curated by the NLM experts. This greatly facilitates the applications of biomedical research and knowledge discovery. Currently, MeSH indexing is manually performed by human experts. To reduce the time and monetary cost associated with manual annotation, many automatic MeSH indexing systems have been proposed to assist manual annotation, including DeepMeSH and NLM\u2019s official model Medical Text Indexer (MTI). However, the existing models usually rely on the intermediate results of other models and suffer from efficiency issues. We propose an end-to-end framework, MeSHProbeNet (formerly named as xgx), which utilizes deep learning and self-attentive MeSH probes to index MeSH terms. Each MeSH probe enables the model to extract one specific aspect of biomedical knowledge from an input article, thus comprehensive biomedical information can be extracted with different MeSH probes and interpretability can be achieved at word level. MeSH terms are finally recommended with a unified classifier, making MeSHProbeNet both time efficient and space efficient.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>MeSHProbeNet won the first place in the latest batch of Task A in the 2018 BioASQ challenge. The result on the last test set of the challenge is reported in this paper. Compared with other state-of-the-art models, such as MTI and DeepMeSH, MeSHProbeNet achieves the highest scores in all the F-measures, including Example Based F-Measure, Macro F-Measure, Micro F-Measure, Hierarchical F-Measure and Lowest Common Ancestor F-measure. We also intuitively show how MeSHProbeNet is able to extract comprehensive biomedical knowledge from an input article.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btz142","type":"journal-article","created":{"date-parts":[[2019,3,7]],"date-time":"2019-03-07T04:22:53Z","timestamp":1551932573000},"page":"3794-3802","source":"Crossref","is-referenced-by-count":37,"title":["MeSHProbeNet: a self-attentive probe net for MeSH indexing"],"prefix":"10.1093","volume":"35","author":[{"given":"Guangxu","family":"Xun","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Virginia , Charlottesville, VA, USA"}]},{"given":"Kishlay","family":"Jha","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Virginia , Charlottesville, VA, USA"}]},{"given":"Ye","family":"Yuan","sequence":"additional","affiliation":[{"name":"Department of Information and Communication Engineering, Beijing University of Technology , Beijing, China"}]},{"given":"Yaqing","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, SUNY at Buffalo , Buffalo, NY, USA"}]},{"given":"Aidong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Virginia , Charlottesville, VA, USA"}]}],"member":"286","published-online":{"date-parts":[[2019,3,9]]},"reference":[{"key":"2023020108350953400_btz142-B1","first-page":"229","article-title":"An overview of metamap: historical perspective and recent advances","volume":"17","author":"Aronson","year":"2010","journal-title":"JAMIA"},{"key":"2023020108350953400_btz142-B2","first-page":"268","volume-title":"MEDINFO 2004\u2014Proceedings of the 11th World Congress on Medical Informatics, San Francisco, California, USA, September 7\u201311, 2004, Volume 107 of Studies in Health Technology and Informatics","author":"Aronson","year":"2004"},{"year":"2014","author":"Bahdanau","key":"2023020108350953400_btz142-B3"},{"key":"2023020108350953400_btz142-B4","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1007\/3-540-33486-6_6","volume-title":"Innovations in Machine Learning","author":"Bengio","year":"2006"},{"first-page":"103","year":"2014","author":"Cho","key":"2023020108350953400_btz142-B5"},{"year":"2014","author":"Chung","key":"2023020108350953400_btz142-B6"},{"key":"2023020108350953400_btz142-B7","doi-asserted-by":"crossref","first-page":"D7.","DOI":"10.1093\/nar\/gkv1290","article-title":"Database resources of the national center for biotechnology information","volume":"44","author":"Coordinators","year":"2016","journal-title":"Nucleic Acids Res"},{"key":"2023020108350953400_btz142-B8","doi-asserted-by":"crossref","first-page":"2103","DOI":"10.1093\/bioinformatics\/btx837","article-title":"Towards self-learning based hypotheses generation in biomedical text domain","volume":"34","author":"Gopalakrishnan","year":"2018","journal-title":"Bioinformatics"},{"key":"2023020108350953400_btz142-B9","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"year":"2017","author":"Huang","key":"2023020108350953400_btz142-B10"},{"first-page":"1965","year":"2017","author":"Jha","key":"2023020108350953400_btz142-B11"},{"first-page":"47","year":"2018","author":"Jin","key":"2023020108350953400_btz142-B12"},{"key":"2023020108350953400_btz142-B13","doi-asserted-by":"crossref","first-page":"820","DOI":"10.1007\/s10618-014-0382-x","article-title":"Evaluation measures for hierarchical classification: a unified view and novel approaches","volume":"29","author":"Kosmopoulos","year":"2015","journal-title":"Data Mining Knowl. 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