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The visualization of Orphadata neurology phenotypes
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,...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911440/ https://www.ncbi.nlm.nih.gov/pubmed/36778102 http://dx.doi.org/10.3389/fdgth.2023.1064936 |
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author | Hier, Daniel B. Yelugam, Raghu Carrithers, Michael D. Wunsch, Donald C. |
author_facet | Hier, Daniel B. Yelugam, Raghu Carrithers, Michael D. Wunsch, Donald C. |
author_sort | Hier, Daniel B. |
collection | PubMed |
description | 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 Orphadata 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 superclasses. 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 Orphadata. Subsumption can collapse phenotype features into superclasses, phenotype lists can be vectorized, and phenotypes vectors can be visualized as heat maps and word clouds. |
format | Online Article Text |
id | pubmed-9911440 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99114402023-02-11 The visualization of Orphadata neurology phenotypes Hier, Daniel B. Yelugam, Raghu Carrithers, Michael D. Wunsch, Donald C. Front Digit Health Digital Health 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 Orphadata 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 superclasses. 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 Orphadata. Subsumption can collapse phenotype features into superclasses, phenotype lists can be vectorized, and phenotypes vectors can be visualized as heat maps and word clouds. Frontiers Media S.A. 2023-01-27 /pmc/articles/PMC9911440/ /pubmed/36778102 http://dx.doi.org/10.3389/fdgth.2023.1064936 Text en © 2023 Hier, Yelugam, Carrithers and Wunsch. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Digital Health Hier, Daniel B. Yelugam, Raghu Carrithers, Michael D. Wunsch, Donald C. The visualization of Orphadata neurology phenotypes |
title | The visualization of Orphadata neurology phenotypes |
title_full | The visualization of Orphadata neurology phenotypes |
title_fullStr | The visualization of Orphadata neurology phenotypes |
title_full_unstemmed | The visualization of Orphadata neurology phenotypes |
title_short | The visualization of Orphadata neurology phenotypes |
title_sort | visualization of orphadata neurology phenotypes |
topic | Digital Health |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9911440/ https://www.ncbi.nlm.nih.gov/pubmed/36778102 http://dx.doi.org/10.3389/fdgth.2023.1064936 |
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