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
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

152-155

Informations de copyright

Georg Thieme Verlag KG Stuttgart.

Déclaration de conflit d'intérêts

Disclosure The authors report no conflicts of interest in this work.

Références

J Am Med Inform Assoc. 2018 Jan 1;25(1):54-60
pubmed: 29126253
J Biomed Semantics. 2018 Jan 18;9(1):6
pubmed: 29347969
PLoS One. 2018 Jan 19;13(1):e0191263
pubmed: 29351341
BMC Genomics. 2018 Jan 19;19(Suppl 1):919
pubmed: 29363423
Appl Clin Inform. 2018 Jan;9(1):54-61
pubmed: 29365340
Int J Med Inform. 2018 Mar;111:140-148
pubmed: 29425625
J Biomed Semantics. 2018 Feb 14;9(1):10
pubmed: 29444698
J Biomed Semantics. 2018 Apr 12;9(1):13
pubmed: 29650041
Database (Oxford). 2018 Jan 1;2018:
pubmed: 29688377
BMC Med Inform Decis Mak. 2018 Jul 6;18(1):61
pubmed: 29980203
IEEE J Biomed Health Inform. 2017 Nov 29;22(5):1672-1683
pubmed: 29990071
Med Phys. 2018 Oct;45(10):e854-e862
pubmed: 30144092
Yearb Med Inform. 2018 Aug;27(1):140-145
pubmed: 30157517
Database (Oxford). 2018 Jan 1;2018:1-10
pubmed: 30212910
Int J Med Inform. 2018 Dec;120:50-61
pubmed: 30409346
JMIR Med Inform. 2018 Dec 21;6(4):e52
pubmed: 30578220

Auteurs

Ferdinand Dhombres (F)

Sorbonne Université, Université Paris 13, Sorbonne Paris Cité, INSERM, UMR_S 1142, LIMICS, Paris, France.
Médecine Sorbonne Université, Service de Médecine Fætale, AP-HP/HUEP, Hôpital Armand Trousseau, Paris, France.

Jean Charlet (J)

Sorbonne Université, Université Paris 13, Sorbonne Paris Cité, INSERM, UMR_S 1142, LIMICS, Paris, France.
AP-HP, Delegation for Clinical Research and Innovation, Paris, France.

Articles similaires

Databases, Protein Protein Domains Protein Folding Proteins Deep Learning
Coal Metagenome Phylogeny Bacteria Genome, Bacterial
Humans Artificial Intelligence COVID-19 SARS-CoV-2 Pandemics

Classifications MeSH