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 phenotypebased causative gene prediction and disease prediction.
|Number of pages||16|
|Journal||International Journal of Data Mining and Bioinformatics|
|State||Published - 2017|
Bibliographical noteFunding Information:
This project was supported by the Fundamental Research Funds for the Central Universities (Grant No. 3102016QD003); the National Natural Science Foundation of China (Grant No. 61332014, 61272121); Chemical Sciences, Geosciences and Biosciences Division, Office of Basic Energy Sciences, Office of Science, U.S. Department of Energy (Grant No. DEFG02-91ER20021); U.S. National Science Foundation (Grant No. 1458556); the Northwestern Polytechnical University (Grant No. G2016KY0301); and the National High Technology Research and Development Program of China (Grant No. 2015AA020101, 2015AA020108, 2014AA021505).
© 2017 Inderscience Enterprises Ltd.
- Causative gene prediction
- Disease prediction
- Human phenotpe ontology
- Noise reduction
- Phenotype similarity
- Semantic similarity
ASJC Scopus subject areas
- Information Systems
- Biochemistry, Genetics and Molecular Biology (all)
- Library and Information Sciences