Predicting disease-related genes by path structure and community structure in protein-protein networks

doi:10.1088/1742-5468/aae02b

CSpace

	Predicting disease-related genes by path structure and community structure in protein-protein networks
	Hu, Ke 1; Hu, Jing-Bo 1; Tang, Liang 2,3; Xiang, Ju 2,3,7; Ma, Jin-Long 4; Gao, Yuan-Yuan 2,3; Li, Hui-Jia 5,6; Zhang, Yan 7
	2018-10-01
发表期刊	JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
ISSN	1742-5468
页码	22
摘要	Network-based computational approaches in the prediction of genes that are associated with diseases are of considerable importance in uncovering the molecular basis of human diseases. Here, we proposed a novel diseasegene-prediction method by combining path-based structure with community structure characteristics in human protein-protein networks. A new similarity measure was first proposed that is based on the path and community structures of networks and leverages community structures for disease-gene prediction. Then, the distinguishing capacity of the methods to identify disease genes from non-disease genes was assessed statistically to analyze their ability to predict disease genes. Finally, the new method was applied to disease-gene prediction for several datasets, and the performances of the measures in disease-gene prediction were analyzed, with an emphasis on assessing the effect of community structure on the predictive performance. The results indicated an ability of the new method to predict disease-genes in several networks and within several disease classes. Further, the results reported here confirm that the incorporation of community structures can indeed improve the performance of disease-gene prediction methods.
关键词	random graphs networks protein interaction networks
DOI	10.1088/1742-5468/aae02b
语种	英语
资助项目	construct program of the key discipline in the Hunan province ; Training Program for Excellent Innovative Youth of Changsha ; National Natural Science Foundation of China[61702054] ; National Natural Science Foundation of China[71871233] ; Hunan Provincial Natural Science Foundation of China[2018JJ3568] ; Scientific Research Fund of Education Department of Hunan Province[17A024] ; Scientific Research Project of Hunan Provincial Health and Family Planning Commission of China[C2017013] ; Scientific Research Fund of the Education Department of Hunan Province[17C0180] ; Scientific Research Fund of the Education Department of Hunan Province[17B034] ; Beijing Natural Science Foundation[9182015] ; Hunan key laboratory cultivation base of the research and development of novel pharmaceutical preparations[2016TP1029]
WOS研究方向	Mechanics ; Physics
WOS类目	Mechanics ; Physics, Mathematical
WOS记录号	WOS:000448442700001
出版者	IOP PUBLISHING LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/31707
专题	中国科学院数学与系统科学研究院
通讯作者	Xiang, Ju; Li, Hui-Jia; Zhang, Yan
作者单位	1.Xiangtan Univ, Dept Phys, Xiangtan 411105, Hunan, Peoples R China 2.Changsha Med Univ, Neurosci Res Ctr, Changsha 410219, Hunan, Peoples R China 3.Changsha Med Univ, Dept Basic Med Sci, Changsha 410219, Hunan, Peoples R China 4.Hebei Univ Sci & Technol, Sch Informat Sci & Engn, Shijiazhuang 050018, Hebei, Peoples R China 5.Cent Univ Finance & Econ, Sch Management Sci & Engn, Beijing 100080, Peoples R China 6.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 7.Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China
推荐引用方式 GB/T 7714	Hu, Ke,Hu, Jing-Bo,Tang, Liang,et al. Predicting disease-related genes by path structure and community structure in protein-protein networks[J]. JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT,2018:22.
APA	Hu, Ke.,Hu, Jing-Bo.,Tang, Liang.,Xiang, Ju.,Ma, Jin-Long.,...&Zhang, Yan.(2018).Predicting disease-related genes by path structure and community structure in protein-protein networks.JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT,22.
MLA	Hu, Ke,et al."Predicting disease-related genes by path structure and community structure in protein-protein networks".JOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT (2018):22.