Computational characterization and identification of human polycystic ovary syndrome genes

Human polycystic ovary syndrome (PCOS) is a highly heritable disease regulated by genetic and environmental factors. Identifying PCOS genes is time consuming and costly in wet-lab. Developing an algorithm to predict PCOS candidates will be helpful. In this study, for the first time, we systematicall...

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Bibliographic Details
Published in:Scientific reports 2018-08, Vol.8 (1), p.12949-7, Article 12949
Main Authors: Zhang, Xing-Zhong, Pang, Yan-Li, Wang, Xian, Li, Yan-Hui
Format: Article
Language:English
Subjects:
631/114/1305
631/114/2408
631/114/2410
Computer applications
Environmental factors
Gene regulation
Humanities and Social Sciences
Learning algorithms
multidisciplinary
Polycystic ovary syndrome
Science
Science (multidisciplinary)
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Summary:Human polycystic ovary syndrome (PCOS) is a highly heritable disease regulated by genetic and environmental factors. Identifying PCOS genes is time consuming and costly in wet-lab. Developing an algorithm to predict PCOS candidates will be helpful. In this study, for the first time, we systematically analyzed properties of human PCOS genes. Compared with genes not yet known to be involved in PCOS regulation, known PCOS genes display distinguishing characteristics: (i) they tend to be located at network center; (ii) they tend to interact with each other; (iii) they tend to enrich in certain biological processes. Based on these features, we developed a machine-learning algorithm to predict new PCOS genes. 233 PCOS candidates were predicted with a posterior probability >0.9. Evidence supporting 7 of the top 10 predictions has been found.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-31110-4