Measuring phenotype semantic similarity using Human Phenotype Ontology

Jiajie Peng, Hansheng Xue, Y. Shao, Xuequn Shang, Yadong Wang, Jin Chen
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引用次数: 5

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.
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利用人类表型本体测量表型语义相似度
基于复杂的临床特征和异质性的遗传背景,做出正确的疾病诊断至关重要,但仍具有挑战性。近年来,基于人类表型本体(Human Phenotype Ontology, HPO)的表型相似性已被广泛应用于疾病诊断。然而,现有的测量是基于基于基因本体论的术语相似性模型进行修订的,这些模型没有针对人类表型本体论进行优化。我们提出了一种新的相似性度量方法,称为PhenoSim。我们的模型包括一个降噪组件来模拟嘈杂的患者表型数据,以及一个基于路径约束的信息内容的方法来测量表型语义相似性。评价试验表明,PhenoSim可以提高基于hpo的表型相似性测量的性能。
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