Disease Phenotypes Are Characterized by Signs (What a Physician Observes during the Examination of a Patient) and Symptoms (The Complaints of a Patient to a Physician). Large Repositories of Disease Phenotypes Are Accessible through the Online Mendelian Inheritance of Man, Human Phenotype Ontology, and Orpha data Initiatives. Many of the Diseases in These Datasets Are Neurologic. for Each Repository, the Phenotype of Neurologic Disease is Represented as a List of Concepts of Variable Length Where the Concepts Are Selected from a Restricted Ontology. Visualizations of These Concept Lists Are Not Provided. We Address This Limitation by using Subsumption to Reduce the Number of Descriptive Features from 2,946 Classes into Thirty Super classes. Phenotype Feature Lists of Variable Lengths Were Converted into Fixed-Length Vectors. Phenotype Vectors Were Aggregated into Matrices and Visualized as Heat Maps that Allowed Side-By-Side Disease Comparisons. Individual Diseases (Representing a Row in the Matrix) Were Visualized as Word Clouds. We Illustrate the Utility of This Approach by Visualizing the Neuro-Phenotypes of 32 Dystonic Diseases from Orpha data. Subsumption Can Collapse Phenotype Features into Super classes, Phenotype Lists Can Be Vectorized, and Phenotypes Vectors Can Be Visualized as Heat Maps and Word Clouds.
D. B. Hier et al., "The Visualization of Orphadata Neurology Phenotypes," Frontiers in Digital Health, vol. 5, article no. 1064936, Frontiers Media, Jan 2023.
The definitive version is available at https://doi.org/10.3389/fdgth.2023.1064936
Electrical and Computer Engineering
Keywords and Phrases
feature reduction; heat maps; neurology; ontology; phenotyping; subsumption; visualization
International Standard Serial Number (ISSN)
Article - Journal
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This work is licensed under a Creative Commons Attribution 4.0 License.
27 Jan 2023
U.S. Department of Veterans Affairs, Grant None