Download PDF
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)

   Permissions and Reprints

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.

Keywords

Big data - informatics - health information technology - genomics - ontologies
 

  • References

  • 1 Dhombres F, Charlet J. As Ontologies Reach Maturity, Artificial Intelligence Starts Being Fully Efficient: Findings from the Section on Knowledge Representation and Management for the Yearbook 2018. Yearb Med Inform 2018; 27 (01) 140-5
    Article in Thieme ConnectPubMedGoogle Scholar
  • 2 Arguello Casteleiro M, Demetriou G, Read W, Fernandez Prieto MJ, Maroto N, Maseda Fernandez D. , et al. Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature. J Biomed Semantics 2018; 9 (01) 13
    CrossrefPubMedGoogle Scholar
  • 3 Le KK, Whiteside MD, Hopkins JE, Gannon VPJ, Laing CR. Spfy: an integrated graph database for real-time prediction of bacterial phenotypes and downstream comparative analyses. Database (Oxford) 2018; 2018: 1-10
    PubMedGoogle Scholar
  • 4 Osumi-Sutherland DJ, Ponta E, Courtot M, Parkinson H, Badi L. Using OWL reasoning to support the generation of novel gene sets for enrichment analysis. J Biomed Semantics 2018; 9 (01) 10
    CrossrefPubMedGoogle Scholar
  • 5 Yu S, Ma Y, Gronsbell J, Cai T, Ananthakrishnan AN, Gainer VS. , et al. Enabling phenotypic big data with PheNorm. J Am Med Inform Assoc 2018; 25 (01) 54-60
    CrossrefPubMedGoogle Scholar
  • 6 Jing X, Hardiker NR, Kay S, Gao Y. Identifying Principles for the Construction of an Ontology-Based Knowledge Base: A Case Study Approach. JMIR Med Inform. 2018; 6 (04) e52
    CrossrefPubMedGoogle Scholar
  • 7 Kohler S. Improved ontology-based similarity calculations using a study-wise annotation model. Database (Oxford) 2018;2018
    PubMed
  • 8 Cheng L, Jiang Y, Ju H, Sun J, Peng J, Zhou M. , et al. InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk. BMC Genomics 2018; 19 (Suppl 1): 919
    CrossrefPubMedGoogle Scholar
  • 9 Viani N, Larizza C, Tibollo V, Napolitano C, Priori SG, Bellazzi R. , et al. Information extraction from Italian medical reports: An ontology-driven approach. Int J Med Inform 2018; 111: 140-8
    CrossrefPubMedGoogle Scholar
  • 10 Bibault JE, Zapletal E, Rance B, Giraud P, Burgun A. Labeling for Big Data in radiation oncology: The Radiation Oncology Structures ontology. PLoS One 2018; 13 (01) e0191263
    CrossrefPubMedGoogle Scholar
  • 11 Matentzoglu N, Malone J, Mungall C, Stevens R. MIRO: guidelines for minimum information for the reporting of an ontology. J Biomed Semantics 2018; 9 (01) 6
    CrossrefPubMedGoogle Scholar
  • 12 Traverso A, van Soest J, Wee L, Dekker A. The radiation oncology ontology (ROO): Publishing linked data in radiation oncology using semantic web and ontology techniques. Med Phys 2018; 45 (10) e854-e62
    CrossrefPubMedGoogle Scholar
  • 13 Catling F, Spithourakis GP, Riedel S. Towards automated clinical coding. Int J Med Inform 2018; 120: 50-61
    CrossrefPubMedGoogle Scholar
  • 14 Maier C, Lang L, Storf H, Vormstein P, Bieber R, Bernarding J. , et al. Towards Implementation of OMOP in a German University Hospital Consortium. Appl Clin Inform 2018; 9 (01) 54-61
    Article in Thieme ConnectPubMedGoogle Scholar
  • 15 Al Kawam A, Sen A, Datta A, Dickey N. Understanding the Bioinformatics Challenges of Integrating Genomics into Healthcare. IEEE J Biomed Health Inform 2018; 22 (05) 1672-83
    CrossrefPubMedGoogle Scholar
  • 16 Zhao Y, Fesharak NJ, Liu H, Luo J. Using data-driven sublanguage pattern mining to induce knowledge models: application in medical image reports knowledge representation. BMC Med Inform Decis Mak 2018; 18 (01) 61
    CrossrefPubMedGoogle Scholar