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PhenoMiner: from text to a database of phenotypes associated with OMIM diseases


Analysis of scientific and clinical phenotypes reported in the experimental literature has been curated manually to build high-quality databases such as the Online Mendelian Inheritance in Man (OMIM). However, the identification and harmonization of phenotype descriptions struggles with the diversity of human expressivity. We introduce a novel automated extraction approach called PhenoMiner that exploits full parsing and conceptual analysis. Apriori association mining is then used to identify relationships to human diseases. We applied PhenoMiner to the BMC open access collection and identified 13 636 phenotype candidates. We identified 28 155 phenotype-disorder hypotheses covering 4898 phenotypes and 1659 Mendelian disorders. Analysis showed: (i) the semantic distribution of the extracted terms against linked ontologies; (ii) a comparison of term overlap with the Human Phenotype Ontology (HP); (iii) moderate support for phenotype-disorder pairs in both OMIM and the literature; (iv) strong associations of phenotype-disorder pairs to known disease-genes pairs using PhenoDigm. The full list of PhenoMiner phenotypes (S1), phenotype-disorder associations (S2), association-filtered linked data (S3) and user database documentation (S5) is available as supplementary data and can be downloaded at under a Creative Commons Attribution 4.0 license.Database URL:

Type Journal
ISBN 1758-0463 (Electronic) 1758-0463 (Linking)
Authors Collier, N.; Groza, T.; Smedley, D.; Robinson, P. N.; Oellrich, A.; Rebholz-Schuhmann, D.;
Responsible Garvan Author Dr Tudor Groza
Publisher Name Database-The Journal of Biological Databases and Curation
Published Date 2015-01-01 00:00:00
Published Volume 2015
Published Pages 1-12
Status Published in-print
URL link to publisher's version
OpenAccess link to author's accepted manuscript version