Feb 28, 2019

Ontology based text mining of gene-phenotype associations: application to candidate gene prediction

Database : the Journal of Biological Databases and Curation
Şenay Kafkas, Robert Hoehndorf

Abstract

Gene-phenotype associations play an important role in understanding the disease mechanisms which is a requirement for treatment development. A portion of gene-phenotype associations are observed mainly experimentally and made publicly available through several standard resources such as MGI. However, there is still a vast amount of gene-phenotype associations buried in the biomedical literature. Given the large amount of literature data, we need automated text mining tools to alleviate the burden in manual curation of gene-phenotype associations and to develop comprehensive resources. In this study, we present an ontology-based approach in combination with statistical methods to text mine gene-phenotype associations from the literature. Our method achieved AUC values of 0.90 and 0.75 in recovering known gene-phenotype associations from HPO and MGI respectively. We posit that candidate genes and their relevant diseases should be expressed with similar phenotypes in publications. Thus, we demonstrate the utility of our approach by predicting disease candidate genes based on the semantic similarities of phenotypes associated with genes and diseases. To the best of our knowledge, this is the first study using an ontology based appr...Continue Reading

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Citations

Mentioned in this Paper

Study
Colony-Stimulating Factors
Genes
4-hydroxy-1-phenyl-1-octanone
Gene Products, Protein
Candidate Disease Gene
Candidate Gene Identification
Evaluation
Gene Type
Literature

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