Abstract
It is critical yet remains to be challenging to make right disease diagnosis based on complex clinical characteristic and heterogeneous genetic background. Recently, Human Phenotype Ontology (HPO)-based phenotype similarity has been widely used to aid disease diagnosis. However, the existing measurements are revised based on the Gene Ontology-based term similarity models, which are not optimized 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 measuring phenotype semantics similarity. Evaluation tests showed that PhenoSim could improve the performance of HPO-based phenotype similarity measurement.
Original language | English |
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Title of host publication | Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 |
Editors | Kevin Burrage, Qian Zhu, Yunlong Liu, Tianhai Tian, Yadong Wang, Xiaohua Tony Hu, Qinghua Jiang, Jiangning Song, Shinichi Morishita, Kevin Burrage, Guohua Wang |
Pages | 763-766 |
Number of pages | 4 |
ISBN (Electronic) | 9781509016105 |
DOIs | |
State | Published - Jan 17 2017 |
Event | 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China Duration: Dec 15 2016 → Dec 18 2016 |
Publication series
Name | Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 |
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Conference
Conference | 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 |
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Country/Territory | China |
City | Shenzhen |
Period | 12/15/16 → 12/18/16 |
Bibliographical note
Publisher Copyright:© 2016 IEEE.
ASJC Scopus subject areas
- Genetics
- Medicine (miscellaneous)
- Genetics(clinical)
- Biochemistry, medical
- Biochemistry
- Molecular Medicine
- Health Informatics