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Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO

Authors
  • Xue, Hansheng1, 2
  • Peng, Jiajie1
  • Shang, Xuequn1
  • 1 School of Computer Science, Northwestern Polytechnical University, Xi’an, China , Xi’an (China)
  • 2 School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China , Shenzhen (China)
Type
Published Article
Journal
BMC Systems Biology
Publisher
Springer (Biomed Central Ltd.)
Publication Date
Apr 05, 2019
Volume
13
Issue
Suppl 2
Identifiers
DOI: 10.1186/s12918-019-0697-8
Source
Springer Nature
Keywords
License
Green

Abstract

BackgroundImproving efficiency of disease diagnosis based on phenotype ontology is a critical yet challenging research area. Recently, Human Phenotype Ontology (HPO)-based semantic similarity has been affectively and widely used to identify causative genes and diseases. However, current phenotype similarity measurements just consider the annotations and hierarchy structure of HPO, neglecting the definition description of phenotype terms.ResultsIn this paper, we propose a novel phenotype similarity measurement, termed as DisPheno, which adequately incorporates the definition of phenotype terms in addition to HPO structure and annotations to measure the similarity between phenotype terms. DisPheno also integrates phenotype term associations into phenotype-set similarity measurement using gene and disease annotations of phenotype terms.ConclusionsCompared with five existing state-of-the-art methods, DisPheno shows great performance in HPO-based phenotype semantic similarity measurement and improves the efficiency of disease identification, especially on noisy patients dataset.

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