Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook
21 August 2020 (online)
Objective: To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2019.
Methods: A comprehensive and standardized review of the biomedical informatics literature was performed to select the most interesting papers of KRM published in 2019, based on PubMed and ISI Web Of Knowledge queries.
Results: Four best papers were selected among 1,189 publications retrieved, following the usual International Medical Informatics Association Yearbook reviewing process. In 2019, research areas covered by pre-selected papers were represented by the design of semantic resources (methods, visualization, curation) and the application of semantic representations for the integration/enrichment of biomedical data. Besides new ontologies and sound methodological guidance to rethink knowledge bases design, we observed large scale applications, promising results for phenotypes characterization, semantic-aware machine learning solutions for biomedical data analysis, and semantic provenance information representations for scientific reproducibility evaluation.
Conclusion: In the KRM selection for 2019, research on knowledge representation demonstrated significant contributions both in the design and in the application of semantic resources. Semantic representations serve a great variety of applications across many medical domains, with actionable results.
- 1 Dhombres F, Charlet J. Formal Medical Knowledge Representation Supports Deep Learning Algorithms, Bioinformatics Pipelines, Genomics Data Analysis, and Big Data Processes. Yearb Med Inform 2019; 28 (01) 152-5
- 2 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
- 3 Dhombres F, Charlet J. Knowledge Representation and Management, It’s Time to Integrate. Yearb Med Inform 2017; 26 (01) 148-51
- 4 Burek P, Scherf N, Herre H. Ontology patterns for the representation of quality changes of cells in time. J Biomed Semantics 2019; 10 (01) 16
- 5 Denaxas S, Gonzalez-Izquierdo A, Direk K, Fitzpatrick NK, Fatemifar G, Banerjee A. et al. UK phenomics platform for developing and validating electronic health record phenotypes: CALIBER. J Am Med Inform Assoc 2019; 26 (12) 1545-59
- 6 Rector AL, Schulz S, Rodrigues JM, Chute CG, Solbrig H. On beyond Gruber: “Ontologies” in today’s biomedical information systems and the limits of OWL. J Biomed Informatics: X 2019; 2: 100002
- 7 Shen F, Zhao Y, Wang L, Mojarad MR, Wang Y, Liu S. et al. Rare disease knowledge enrichment through a data-driven approach. BMC Med Inform Decis Mak 2019; 19 (01) 32
- 8 Babbi G, Martelli PL, Casadio R. PhenPath: a tool for characterizing biological functions underlying different phenotypes. BMC Genomics 2019; 20 (Suppl 8): 548
- 9 Brenas JH, Shin EK, Shaban-Nejad A. Adverse Childhood Experiences Ontology for Mental Health Surveillance, Research, and Evaluation: Advanced Knowledge Representation and Semantic Web Techniques. JMIR Ment Health 2019; 6 (05) e13498
- 10 Doing-Harris K, Bray BE, Thackeray A, Shah RU, Shao Y, Cheng Y. et al. Development of a cardiac-centered frailty ontology. J Biomed Semantics 2019; 10 (01) 3
- 11 Hoyt CT, Domingo-Fernandez D, Aldisi R, Xu L, Kolpeja K, Spalek S. et al. Re-curation and rational enrichment of knowledge graphs in Biological Expression Language. Database (Oxford) 2019; 2019: baz068
- 12 Jackson RC, Balhoff JP, Douglass E, Harris NL, Mungall CJ, Overton JA. ROBOT: A Tool for Automating Ontology Workflows. BMC Bioinformatics 2019; 20 (01) 407
- 13 Kuznetsova I, Lugmayr A, Siira SJ, Rackham O, Filipovska A. CirGO: an alternative circular way of visualising gene ontology terms. BMC Bioinformatics 2019; 20 (01) 84
- 14 Lamurias A, Sousa D, Clarke LA, Couto FM. BO-LSTM: classifying relations via long short-term memory networks along biomedical ontologies. BMC Bioinformatics 2019; 20 (01) 10
- 15 Sahoo SS, Valdez J, Kim M, Rueschman M, Redline S. ProvCaRe: Characterizing scientific reproducibility of biomedical research studies using semantic provenance metadata. Int J Med Inform 2019; 121: 10-8
- 16 Siegele DA, LaBonte SA, Wu PI, Chibucos MC, Nandendla S, Giglio MG. et al. Phenotype annotation with the ontology of microbial phenotypes (OMP). J Biomed Semantics 2019; 10 (01) 13
- 17 Smaili FZ, Gao X, Hoehndorf R. Formal axioms in biomedical ontologies improve analysis and interpretation of associated data. Bioinformatics 2020; 36 (07) 2229-36
- 18 Yu H, Nysak S, Garg N, Ong E, Ye X, Zhang X. et al. ODAE: Ontology-based systematic representation and analysis of drug adverse events and its usage in study of adverse events given different patient age and disease conditions. BMC Bioinformatics 2019; 20 (Suppl 7): 199