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Yearb Med Inform. 2019 Aug;28(1):152-155. doi: 10.1055/s-0039-1677933. Epub 2019 Aug 16.

Formal Medical Knowledge Representation Supports Deep Learning Algorithms, Bioinformatics Pipelines, Genomics Data Analysis, and Big Data Processes.

Author information

1
Sorbonne Université, Université Paris 13, Sorbonne Paris Cité, INSERM, UMR_S 1142, LIMICS, Paris, France.
2
Médecine Sorbonne Université, Service de Médecine Fætale, AP-HP/HUEP, Hôpital Armand Trousseau, Paris, France.
3
AP-HP, Delegation for Clinical Research and Innovation, Paris, France.

Abstract

OBJECTIVE:

To select, present, and summarize the best papers published in 2018 in the field of Knowledge Representation and Management (KRM).

METHODS:

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.

RESULTS:

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.

CONCLUSION:

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.

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