#6158. Linking common human diseases to their phenotypes; development of a resource for human phenomics

October 2026publication date
Proposal available till 11-06-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Computer Networks and Communications;
Computer Science Applications;
Information Systems;
Health Informatics;
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Abstract:
Background: In recent years a large volume of clinical genomics data has become available due to rapid advances in sequencing technologies. Efficient exploitation of this genomics data requires linkage to patient phenotype profiles. Current resources providing disease-phenotype associations are not comprehensive, and they often do not have broad coverage of the disease terminologies, particularly ICD-10, which is still the primary terminology used in clinical settings. Methods: We developed two approaches to gather disease-phenotype associations. First, we used a text mining method that utilizes semantic relations in phenotype ontologies, and applies statistical methods to extract associations between diseases in ICD-10 and phenotype ontology classes from the literature. Second, we developed a semi-automatic way to collect ICD-10–phenotype associations from existing resources containing known relationships.
Keywords:
Disease–phenotype associations; Ontologies; Text mining; UK Biobank

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