COS: A new MeSH term embedding incorporating corpus, ontology, and semantic predications.

The embedding of Medical Subject Headings (MeSH) terms has become a foundation for many downstream bioinformatics tasks. Recent studies employ different data sources, such as the corpus (in which each document is indexed by a set of MeSH terms), the MeSH term ontology, and the semantic predications...

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Autores principales:	Juncheng Ding, Wei Jin
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/41893d8e96ad44e985f34d6b844157d2
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id	oai:doaj.org-article:41893d8e96ad44e985f34d6b844157d2
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spelling	oai:doaj.org-article:41893d8e96ad44e985f34d6b844157d22021-11-25T06:19:21ZCOS: A new MeSH term embedding incorporating corpus, ontology, and semantic predications.1932-620310.1371/journal.pone.0251094https://doaj.org/article/41893d8e96ad44e985f34d6b844157d22021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251094https://doaj.org/toc/1932-6203The embedding of Medical Subject Headings (MeSH) terms has become a foundation for many downstream bioinformatics tasks. Recent studies employ different data sources, such as the corpus (in which each document is indexed by a set of MeSH terms), the MeSH term ontology, and the semantic predications between MeSH terms (extracted by SemMedDB), to learn their embeddings. While these data sources contribute to learning the MeSH term embeddings, current approaches fail to incorporate all of them in the learning process. The challenge is that the structured relationships between MeSH terms are different across the data sources, and there is no approach to fusing such complex data into the MeSH term embedding learning. In this paper, we study the problem of incorporating corpus, ontology, and semantic predications to learn the embeddings of MeSH terms. We propose a novel framework, Corpus, Ontology, and Semantic predications-based MeSH term embedding (COS), to generate high-quality MeSH term embeddings. COS converts the corpus, ontology, and semantic predications into MeSH term sequences, merges these sequences, and learns MeSH term embeddings using the sequences. Extensive experiments on different datasets show that COS outperforms various baseline embeddings and traditional non-embedding-based baselines.Juncheng DingWei JinPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 5, p e0251094 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Juncheng Ding Wei Jin COS: A new MeSH term embedding incorporating corpus, ontology, and semantic predications.
description	The embedding of Medical Subject Headings (MeSH) terms has become a foundation for many downstream bioinformatics tasks. Recent studies employ different data sources, such as the corpus (in which each document is indexed by a set of MeSH terms), the MeSH term ontology, and the semantic predications between MeSH terms (extracted by SemMedDB), to learn their embeddings. While these data sources contribute to learning the MeSH term embeddings, current approaches fail to incorporate all of them in the learning process. The challenge is that the structured relationships between MeSH terms are different across the data sources, and there is no approach to fusing such complex data into the MeSH term embedding learning. In this paper, we study the problem of incorporating corpus, ontology, and semantic predications to learn the embeddings of MeSH terms. We propose a novel framework, Corpus, Ontology, and Semantic predications-based MeSH term embedding (COS), to generate high-quality MeSH term embeddings. COS converts the corpus, ontology, and semantic predications into MeSH term sequences, merges these sequences, and learns MeSH term embeddings using the sequences. Extensive experiments on different datasets show that COS outperforms various baseline embeddings and traditional non-embedding-based baselines.
format	article
author	Juncheng Ding Wei Jin
author_facet	Juncheng Ding Wei Jin
author_sort	Juncheng Ding
title	COS: A new MeSH term embedding incorporating corpus, ontology, and semantic predications.
title_short	COS: A new MeSH term embedding incorporating corpus, ontology, and semantic predications.
title_full	COS: A new MeSH term embedding incorporating corpus, ontology, and semantic predications.
title_fullStr	COS: A new MeSH term embedding incorporating corpus, ontology, and semantic predications.
title_full_unstemmed	COS: A new MeSH term embedding incorporating corpus, ontology, and semantic predications.
title_sort	cos: a new mesh term embedding incorporating corpus, ontology, and semantic predications.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/41893d8e96ad44e985f34d6b844157d2
work_keys_str_mv	AT junchengding cosanewmeshtermembeddingincorporatingcorpusontologyandsemanticpredications AT weijin cosanewmeshtermembeddingincorporatingcorpusontologyandsemanticpredications
_version_	1718413858294464512

COS: A new MeSH term embedding incorporating corpus, ontology, and semantic predications.

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