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

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Abstract

Background: Improving 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. Results: In 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. Conclusions: Compared 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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Xue, H., Peng, J., & Shang, X. (2019). Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO. BMC Systems Biology, 13. https://doi.org/10.1186/s12918-019-0697-8

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