Computational characterization and identification of human polycystic ovary syndrome genes

Abstract 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 syst...

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Autores principales:	Xing-Zhong Zhang, Yan-Li Pang, Xian Wang, Yan-Hui Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/f41f5195b8504573973922fd5d6f602e
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spelling	oai:doaj.org-article:f41f5195b8504573973922fd5d6f602e2021-12-02T15:05:03ZComputational characterization and identification of human polycystic ovary syndrome genes10.1038/s41598-018-31110-42045-2322https://doaj.org/article/f41f5195b8504573973922fd5d6f602e2018-08-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-31110-4https://doaj.org/toc/2045-2322Abstract 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.Xing-Zhong ZhangYan-Li PangXian WangYan-Hui LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-7 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xing-Zhong Zhang Yan-Li Pang Xian Wang Yan-Hui Li Computational characterization and identification of human polycystic ovary syndrome genes
description	Abstract 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.
format	article
author	Xing-Zhong Zhang Yan-Li Pang Xian Wang Yan-Hui Li
author_facet	Xing-Zhong Zhang Yan-Li Pang Xian Wang Yan-Hui Li
author_sort	Xing-Zhong Zhang
title	Computational characterization and identification of human polycystic ovary syndrome genes
title_short	Computational characterization and identification of human polycystic ovary syndrome genes
title_full	Computational characterization and identification of human polycystic ovary syndrome genes
title_fullStr	Computational characterization and identification of human polycystic ovary syndrome genes
title_full_unstemmed	Computational characterization and identification of human polycystic ovary syndrome genes
title_sort	computational characterization and identification of human polycystic ovary syndrome genes
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/f41f5195b8504573973922fd5d6f602e
work_keys_str_mv	AT xingzhongzhang computationalcharacterizationandidentificationofhumanpolycysticovarysyndromegenes AT yanlipang computationalcharacterizationandidentificationofhumanpolycysticovarysyndromegenes AT xianwang computationalcharacterizationandidentificationofhumanpolycysticovarysyndromegenes AT yanhuili computationalcharacterizationandidentificationofhumanpolycysticovarysyndromegenes
_version_	1718388991807455232

Computational characterization and identification of human polycystic ovary syndrome genes

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