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This review highlights top 2018 research in Knowledge Representation and Management (KRM). Semantic representations show promise for deep learning, bioinformatics, and analyzing genomic and phenotypic big data.

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Area of Science:

  • Medical Informatics
  • Knowledge Representation and Management (KRM)

Background:

  • The field of Knowledge Representation and Management (KRM) is rapidly evolving.
  • Identifying seminal works is crucial for tracking advancements.

Purpose of the Study:

  • To identify, present, and summarize the most impactful papers in KRM published in 2018.
  • To provide a curated overview of key research trends.

Main Methods:

  • A systematic review of the medical informatics literature.
  • Searches conducted on PubMed and ISI Web of Knowledge databases.
  • Selection based on a standardized review process.

Main Results:

  • Four outstanding papers were selected from 962 publications.
  • Key research areas included ontology-based data integration for phenotype-genotype associations, ontology design and application, and clinical text semantic annotation.
  • Semantic representations showed significant value.

Conclusions:

  • Semantic representations enhance deep learning for text mining and bioinformatics pipelines.
  • Ontologies enrich the analysis of whole genome expression data.
  • Semantic representations show potential for processing phenotypic big data.