Formal Medical Knowledge Representation Supports Deep Learning Algorithms, Bioinformatics Pipelines, Genomics Data Analysis, and Big Data Processes.
Journal
Yearbook of medical informatics
ISSN: 2364-0502
Titre abrégé: Yearb Med Inform
Pays: Germany
ID NLM: 9312666
Informations de publication
Date de publication:
Aug 2019
Aug 2019
Historique:
entrez:
17
8
2019
pubmed:
17
8
2019
medline:
18
12
2019
Statut:
ppublish
Résumé
To select, present, and summarize the best papers published in 2018 in the field of Knowledge Representation and Management (KRM). A comprehensive and standardized review of the medical informatics literature was performed to select the most interesting papers published in 2018 in KRM, based on PubMed and ISI Web Of Knowledge queries. Four best papers were selected among the 962 publications retrieved following the Yearbook review process. The research areas in 2018 were mainly related to the ontology-based data integration for phenotype-genotype association mining, the design of ontologies and their application, and the semantic annotation of clinical texts. In the KRM selection for 2018, research on semantic representations demonstrated their added value for enhanced deep learning approaches in text mining and for designing novel bioinformatics pipelines based on graph databases. In addition, the ontology structure can enrich the analyses of whole genome expression data. Finally, semantic representations demonstrated promising results to process phenotypic big data.
Identifiants
pubmed: 31419827
doi: 10.1055/s-0039-1677933
pmc: PMC6697514
doi:
Types de publication
Journal Article
Review
Langues
eng
Sous-ensembles de citation
IM
Pagination
152-155Informations de copyright
Georg Thieme Verlag KG Stuttgart.
Déclaration de conflit d'intérêts
Disclosure The authors report no conflicts of interest in this work.
Références
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pubmed: 29126253
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