CC BY-NC-ND 4.0 · Yearb Med Inform 2019; 28(01): 152-155
DOI: 10.1055/s-0039-1677933
Section 6: Knowledge Representation and Management
Synopsis
Georg Thieme Verlag KG Stuttgart

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

Findings from the 2019 IMIA Yearbook Section on Knowledge Representation and Management
Ferdinand Dhombres
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
,
Jean Charlet
1  Sorbonne Université, Université Paris 13, Sorbonne Paris Cité, INSERM, UMR_S 1142, LIMICS, Paris, France
3  AP-HP, Delegation for Clinical Research and Innovation, Paris, France
,
Section Editors for the IMIA Yearbook Section on Knowledge Representation and Management› Author Affiliations
Further Information

Publication History

Publication Date:
16 August 2019 (online)

  

Summary

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.