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Klarigi: Characteristic explanations for semantic biomedical data.

Karin Slater1, John A Williams2, Paul N Schofield3

  • 1College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, University of Birmingham, UK; Institute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, UK; MRC Health Data Research UK (HDR UK), Midlands, UK; University Hospitals Birmingham NHS Foundation Trust, Edgbaston, Birmingham, UK.

Computers in Biology and Medicine
|January 13, 2023
PubMed
Summary
This summary is machine-generated.

Klarigi, a new tool, enhances biomedical data analysis by identifying characteristic and discriminatory ontology classes for entity groups. It offers novel multivariable explanations, improving insights beyond traditional enrichment methods.

Keywords:
Enrichment analysisExplicabilityOntologyPhenotype profilesPhenotypesSemantic analysisSemantic explanation

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Area of Science:

  • Biomedical informatics
  • Computational biology
  • Ontology engineering

Background:

  • Biomedical entity annotation with ontology classes enables formal semantic analysis and knowledge mobilization.
  • Current enrichment analysis methods for ontology annotations primarily focus on univariate relationships and have limitations in capturing complex semantic features and explanatory power.
  • Existing approaches struggle to derive cohesive, characteristic, and discriminatory sets of ontology classes for biomedical entity groups.

Purpose of the Study:

  • To introduce Klarigi, a novel tool designed for identifying compositional and discriminatory ontology classes for annotated biomedical entity groups.
  • To develop a new algorithm for deriving multivariable semantic explanations for entity groups, leveraging semantic inference and an ontology reasoner.
  • To provide a classification method for evaluating the discriminatory power of candidate class sets and compare Klarigi's performance against traditional enrichment methods.

Main Methods:

  • Klarigi employs multiple scoring heuristics for identifying ontology classes that are both compositional and discriminatory.
  • A novel algorithm for multivariable semantic explanation derivation is implemented, utilizing live ontology reasoner inference.
  • The tool incorporates a classification method for assessing the discriminatory power of candidate class sets and includes significance testing.

Main Results:

  • Klarigi successfully produces characteristic and discriminatory explanations for biomedical entity groups in two distinct clinical test cases.
  • The generated explanations recapitulate and extend existing knowledge found in biomedical databases and literature for several diseases.
  • Klarigi demonstrates a distinct advantage over traditional enrichment methods in exploring biomedical datasets.

Conclusions:

  • Klarigi offers a valuable new perspective for exploring biomedical datasets, surpassing the limitations of traditional enrichment analysis.
  • The tool's ability to derive multivariable semantic explanations enhances the understanding of relationships within biomedical data.
  • Klarigi contributes to improved insights into semantic biomedical data, aiding in tasks like differential diagnosis and variant prioritization.