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

Publisher: Biomed Central Country of Publication: England NLM ID: 101531992 Publication Model: Electronic Cited Medium: Internet ISSN: 2041-1480 (Electronic) NLM ISO Abbreviation: J Biomed Semantics Subsets: MEDLINE

Original Publication: [London] : Biomed Central

Data Mining* ; Phenomics*; Databases, Factual ; Humans ; Knowledge Bases ; Phenotype

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.
Results: We generated four datasets. Two of them are independent datasets linking diseases to their phenotypes based on text mining and semi-automatic strategies. The remaining two datasets are generated from these datasets and cover a subset of ICD-10 classes of common diseases contained in UK Biobank. We extensively validated our text mined and semi-automatically curated datasets by: comparing them against an expert-curated validation dataset containing disease-phenotype associations, measuring their similarity to disease-phenotype associations found in public databases, and assessing how well they could be used to recover gene-disease associations using phenotype similarity.
Conclusion: We find that our text mining method can produce phenotype annotations of diseases that are correct but often too general to have significant information content, or too specific to accurately reflect the typical manifestations of the sporadic disease. On the other hand, the datasets generated from integrating multiple knowledgebases are more complete (i.e., cover more of the required phenotype annotations for a given disease). We make all data freely available at https://doi.org/10.5281/zenodo.4726713 .
(© 2021. The Author(s).)

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MC_PC_17228 United Kingdom MRC_ Medical Research Council; MC_QA137853 United Kingdom MRC_ Medical Research Council

Keywords: Disease–phenotype associations; Ontologies; Text mining; UK Biobank

Date Created: 20210824 Date Completed: 20211028 Latest Revision: 20220223

20250114

PMC8383460

10.1186/s13326-021-00249-x

34425897