Measuring phenotype semantic similarity using Human Phenotype Ontology

Jiajie Peng; Hansheng Xue; Yukai Shao; Xuequn Shang; Yadong Wang; Jin Chen

doi:10.1109/BIBM.2016.7822617

Measuring phenotype semantic similarity using Human Phenotype Ontology

Jiajie Peng, Hansheng Xue, Yukai Shao, Xuequn Shang, Yadong Wang, Jin Chen

Internal Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

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
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	https://doi.org/10.1109/BIBM.2016.7822617
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

Conference

Conference	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Country/Territory	China
City	Shenzhen
Period	12/15/16 → 12/18/16

Bibliographical note

Funding Information:
This project was supported by 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); the Fundamental Research Funds for the Central Universities (Grant No. 3102016QD003); and the National High Technology Research and Development Program of China (Grant No. 2015AA020101, 2015AA020108, 2014AA021505)

Publisher Copyright:
© 2016 IEEE.

ASJC Scopus subject areas

Genetics
Medicine (miscellaneous)
Genetics(clinical)
Biochemistry, medical
Biochemistry
Molecular Medicine
Health Informatics

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BIBM.2016.7822617

Cite this

Peng, J., Xue, H., Shao, Y., Shang, X., Wang, Y., & Chen, J. (2017). Measuring phenotype semantic similarity using Human Phenotype Ontology. In K. Burrage, Q. Zhu, Y. Liu, T. Tian, Y. Wang, X. T. Hu, Q. Jiang, J. Song, S. Morishita, K. Burrage, & G. Wang (Eds.), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 (pp. 763-766). [7822617] (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). https://doi.org/10.1109/BIBM.2016.7822617

Peng, Jiajie ; Xue, Hansheng ; Shao, Yukai et al. / Measuring phenotype semantic similarity using Human Phenotype Ontology. Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. editor / Kevin Burrage ; Qian Zhu ; Yunlong Liu ; Tianhai Tian ; Yadong Wang ; Xiaohua Tony Hu ; Qinghua Jiang ; Jiangning Song ; Shinichi Morishita ; Kevin Burrage ; Guohua Wang. 2017. pp. 763-766 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

@inproceedings{7ced99ab485143f48092e3c5c1d11d48,

title = "Measuring phenotype semantic similarity using Human Phenotype Ontology",

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.",

author = "Jiajie Peng and Hansheng Xue and Yukai Shao and Xuequn Shang and Yadong Wang and Jin Chen",

note = "Funding Information: This project was supported by 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); the Fundamental Research Funds for the Central Universities (Grant No. 3102016QD003); and the National High Technology Research and Development Program of China (Grant No. 2015AA020101, 2015AA020108, 2014AA021505) Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 ; Conference date: 15-12-2016 Through 18-12-2016",

year = "2017",

month = jan,

day = "17",

doi = "10.1109/BIBM.2016.7822617",

language = "English",

series = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

pages = "763--766",

editor = "Kevin Burrage and Qian Zhu and Yunlong Liu and Tianhai Tian and Yadong Wang and Hu, {Xiaohua Tony} and Qinghua Jiang and Jiangning Song and Shinichi Morishita and Kevin Burrage and Guohua Wang",

booktitle = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

}

Peng, J, Xue, H, Shao, Y, Shang, X, Wang, Y & Chen, J 2017, Measuring phenotype semantic similarity using Human Phenotype Ontology. in K Burrage, Q Zhu, Y Liu, T Tian, Y Wang, XT Hu, Q Jiang, J Song, S Morishita, K Burrage & G Wang (eds), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016., 7822617, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, pp. 763-766, 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Shenzhen, China, 12/15/16. https://doi.org/10.1109/BIBM.2016.7822617

Measuring phenotype semantic similarity using Human Phenotype Ontology. / Peng, Jiajie; Xue, Hansheng; Shao, Yukai et al.
Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. ed. / Kevin Burrage; Qian Zhu; Yunlong Liu; Tianhai Tian; Yadong Wang; Xiaohua Tony Hu; Qinghua Jiang; Jiangning Song; Shinichi Morishita; Kevin Burrage; Guohua Wang. 2017. p. 763-766 7822617 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Measuring phenotype semantic similarity using Human Phenotype Ontology

AU - Peng, Jiajie

AU - Xue, Hansheng

AU - Shao, Yukai

AU - Shang, Xuequn

AU - Wang, Yadong

AU - Chen, Jin

N1 - Funding Information: This project was supported by 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); the Fundamental Research Funds for the Central Universities (Grant No. 3102016QD003); and the National High Technology Research and Development Program of China (Grant No. 2015AA020101, 2015AA020108, 2014AA021505) Publisher Copyright: © 2016 IEEE.

PY - 2017/1/17

Y1 - 2017/1/17

N2 - 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.

AB - 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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DO - 10.1109/BIBM.2016.7822617

M3 - Conference contribution

AN - SCOPUS:85013276128

T3 - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

SP - 763

EP - 766

BT - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

A2 - Burrage, Kevin

A2 - Zhu, Qian

A2 - Liu, Yunlong

A2 - Tian, Tianhai

A2 - Wang, Yadong

A2 - Hu, Xiaohua Tony

A2 - Jiang, Qinghua

A2 - Song, Jiangning

A2 - Morishita, Shinichi

A2 - Burrage, Kevin

A2 - Wang, Guohua

T2 - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Y2 - 15 December 2016 through 18 December 2016

ER -

Peng J, Xue H, Shao Y, Shang X, Wang Y, Chen J. Measuring phenotype semantic similarity using Human Phenotype Ontology. In Burrage K, Zhu Q, Liu Y, Tian T, Wang Y, Hu XT, Jiang Q, Song J, Morishita S, Burrage K, Wang G, editors, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. 2017. p. 763-766. 7822617. (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). doi: 10.1109/BIBM.2016.7822617

Measuring phenotype semantic similarity using Human Phenotype Ontology

Abstract

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Bibliographical note

ASJC Scopus subject areas

UN SDGs

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