Medical Subject Headings (MeSH).

By C. Lipscomb. Bulletin of the Medical Library Association, 2000

1,260 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3418cf24575313d9942af12d1a6f572ac0d34579

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Citations in the corpus

Predicting miRNA‐disease association based on inductive matrix completion (2018, methodology): “A Directed Acyclic Graph (DAG) was constructed to describe a disease based on the MeSH descriptors downloaded from the National Library of Medicine (Lipscomb, 2000).”
Deep Bidirectional Language-Knowledge Graph Pretraining (2022, unknown): “In Table 4, we study down-stream performance when the model capacity is increased— the number of text-KG fusion layers is increased from 5 to 7—for both GreaseLM and D RAGON .”
Biomedical Ontologies in Action: Role in Knowledge Management, Data Integration and Decision Support (2008, unknown): “…[37, 38]; the Medical Subject Headings (MeSH), a controlled vocabulary for the indexing and retrieval of the bio-medical literature [39, 40]; and the Unified Medical Language System (UMLS), a terminology integration sys-tem in which all the above ontologies are integrated (with the…”
webTWAS: a resource for disease candidate susceptibility genes identified by transcriptome-wide association study (2021, methodology): “A convenient web interface allows users to explore gene-and disease-level disease association statistics across multiple studies, and search for related diseases using the integrated MeSH ontology tree (33).”
Medical Question Answering for Clinical Decision Support (2016, methodology): “Both the Unified Medical Language System (UMLS) [6] and MeSH [17] were commonly used as ontological resources for medical concepts.”
The CHEMDNER corpus of chemicals and drugs and its annotation principles (2015, background): “Most of the underlying concept classes were derived from categories found in Medical Subject Headings (MeSH), a hierarchical terminological resource used to index PubMed abstracts [15].”
Graph-Sparse LDA: A Topic Model with Structured Sparsity (2014, methodology): “Keywords for biomedical Figure 1: Simpliﬁed section of the ICD9-CM diagnostic hierarchy.”
Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm (2016, methodology): “Disease similarity Sim d p based on disease phenotypes is calculated using MimMiner (Van Driel et al. , 2006), which measure disease similarity by computing similarity between MeSH terms (Lipscomb, 2000) appearing in the medical description of diseases from the OMIM database.”
Prediction of Drug Combinations by Integrating Molecular and Pharmacological Data (2011, background): “Due to different names and synonyms associated with a disease, we mapped all disease names to Medical Subject Headings (MeSH) [32] terms by exact match, considering only the diseases branch and the psychiatry and psychology branch.”
PREDICT: a method for inferring novel drug indications with application to personalized medicine (2011, unknown): “The phenotypic similarity was constructed by identifying similarity between MeSH terms (Lipscomb, 2000) appearing in the medical description of diseases from the OMIM database (Hamosh et al, 2002).”
Mining Meaning from Wikipedia (2008, unknown): “Topic labels are typically obtained from a domain-specific thesaurus—such as MESH (Lipscomb, 2000) for the Medical domain—because general thesauri like WordNet and Roget are too small to provide sufficient detail.”