Int. J. of Data Mining and Bioinformatics   »   2017 Vol.17, No.2

 

 

Title: A novel method to measure the semantic similarity of HPO terms

 

Authors: Jiajie Peng; Hansheng Xue; Yukai Shao; Xuequn Shang; Yadong Wang; Jin Chen

 

Addresses:
School of Computer Science, Northwestern Polytechnical University, Xi'an, China
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China
School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China
School of Computer Science, Northwestern Polytechnical University, Xi'an, China
School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Institute of Biomedical Informatics, College of Medicine, University of Kentucky, Lexington, KY 40536, USA

 

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.

 

Keywords: human phenotpe ontology; semantic similarity; phenotype similarity; noise reduction; causative gene prediction; disease prediction.

 

DOI: 10.1504/IJDMB.2017.10005213

 

Int. J. of Data Mining and Bioinformatics, 2017 Vol.17, No.2, pp.173 - 188

 

Submission date: 08 Mar 2017
Date of acceptance: 14 Mar 2017
Available online: 22 May 2017

 

 

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