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Predicting disease-related genes by path structure and community structure in protein-protein networks
Hu, Ke1; Hu, Jing-Bo1; Tang, Liang2,3; Xiang, Ju2,3,7; Ma, Jin-Long4; Gao, Yuan-Yuan2,3; Li, Hui-Jia5,6; Zhang, Yan7
2018-10-01
Source PublicationJOURNAL OF STATISTICAL MECHANICS-THEORY AND EXPERIMENT
ISSN1742-5468
Pages22
AbstractNetwork-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.
Keywordrandom graphs networks protein interaction networks
DOI10.1088/1742-5468/aae02b
Language英语
Funding Projectconstruct 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 Research AreaMechanics ; Physics
WOS SubjectMechanics ; Physics, Mathematical
WOS IDWOS:000448442700001
PublisherIOP PUBLISHING LTD
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/31707
Collection中国科学院数学与系统科学研究院
Affiliation1.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
Recommended Citation
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.
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