A novel method to measure the semantic similarity of HPO terms. (2017)

• Record Type: Journal Article
• Authors: Peng, Jiajie; Xue, Hansheng; Shao, Yukai; Shang, Xuequn; Wang, Yadong; Chen, Jin
• Is Part Of: International journal of data mining and bioinformatics. Volume 17:Number 2(2017)
• Page Start: 173
• Page End: 188
• Publication Date: 2017
• Subjects: human phenotpe ontology -- semantic similarity -- phenotype similarity -- noise reduction -- causative gene prediction -- disease prediction; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

Abstract

It is critical yet remains to be challenging to make precise disease diagnosis from complex clinical features and highly heterogeneous genetic background. Recently, phenotype similarity has been effectively applied to model patient phenotype data. However, the existing measurements are revised based on the Gene Ontology-based term similarity models, which are not optimised for human phenotype ontologies. We propose a new similarity measure called PhenoSim. Our model includes a noise reduction component to model the noisy patient phenotype data, and a path-constrained Information Content-based method for phenotype semantics similarity measurement. Evaluation tests compared PhenoSim with four existing approaches. It showed that PhenoSim, could effectively improve the performance of HPO-based phenotype similarity measurement, thus increasing the accuracy of phenotype-based causative gene prediction and disease prediction.