学科主题基础医学
Computational characterization and identification of human polycystic ovary syndrome genes
Zhang, Xing-Zhong1; Pang, Yan-Li3; Wang, Xian1; Li, Yan-Hui2
通讯作者Wang, Xian(1) ; Li, Yan-Hui(2)
刊名SCIENTIFIC REPORTS
2018-08-28
DOI10.1038/s41598-018-31110-4
8
收录类别SCI
文章类型Article
WOS标题词Science & Technology
类目[WOS]Multidisciplinary Sciences
研究领域[WOS]Science & Technology - Other Topics
关键词[WOS]SYNDROME PCOS ; EXPRESSION ; GENETICS ; NETWORK ; WOMEN ; ASSOCIATION ; PREDICTION ; CENTRALITY ; ONTOLOGY ; INSIGHTS
英文摘要

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.

语种英语
WOS记录号WOS:000442917500021
通讯作者邮箱xwang@bjmu.edu.cn ; liyanhui@bjmu.edu.cn
第一作者单位Peking Univ, Sch Basic Med Sci, Dept Physiol & Pathophysiol, Beijing, Peoples R China
通讯作者单位Peking Univ, Sch Basic Med Sci, Dept Physiol & Pathophysiol, Beijing, Peoples R China ; Peking Univ, Inst Cardiovasc Sci, Beijing, Peoples R China
ISSN2045-2322
引用统计
文献类型期刊论文
条目标识符http://ir.bjmu.edu.cn/handle/400002259/142966
专题北京大学基础医学院_生理学与病理生理学系
北京大学基础医学院
北京大学第三临床医学院_妇产科
作者单位1.Peking Univ, Sch Basic Med Sci, Dept Physiol & Pathophysiol, Beijing, Peoples R China;
2.Peking Univ, Inst Cardiovasc Sci, Beijing, Peoples R China;
3.Peking Univ, Hosp 3, Ctr Reprod Med, Dept Obstet & Gynecol, Beijing, Peoples R China
推荐引用方式
GB/T 7714
Zhang, Xing-Zhong,Pang, Yan-Li,Wang, Xian,et al. Computational characterization and identification of human polycystic ovary syndrome genes[J]. SCIENTIFIC REPORTS,2018,8.
APA Zhang, Xing-Zhong,Pang, Yan-Li,Wang, Xian,&Li, Yan-Hui.(2018).Computational characterization and identification of human polycystic ovary syndrome genes.SCIENTIFIC REPORTS,8.
MLA Zhang, Xing-Zhong,et al."Computational characterization and identification of human polycystic ovary syndrome genes".SCIENTIFIC REPORTS 8(2018).
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