Computational characterization and identification of human polycystic ovary syndrome genes

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作者：
Xing-Zhong Zhang
Yan-Li Pang
Xian Wang
Yan-Hui Li
机构：
[1] Peking University,Department of Physiology and Pathophysiology, School of Basic Medical Sciences
[2] Institute of Cardiovascular Sciences,Department of Obstetrics and Gynecology, Center for Reproductive Medicine
[3] Peking University,undefined
[4] Peking University Third Hospital,undefined
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Scientific Reports | / 8卷
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摘要：
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.
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