A Novel Candidate Disease Genes Prioritization Method Based on Module Partition and Rank Fusion

doi:10.1089/omi.2009.0143

CORC > 数学与系统科学研究院 > 中国科学院数学与系统科学研究院 > 应用数学研究所

	A Novel Candidate Disease Genes Prioritization Method Based on Module Partition and Rank Fusion
	Chen, Xing 1,2; Yan, Gui-Ying1 ; Liao, Xiao-Ping 1,2
刊名	OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY
	2010-08-01
卷号	14 期号:4 页码:337-356
ISSN号	1536-2310
DOI	10.1089/omi.2009.0143
英文摘要	Identifying disease genes is very important not only for better understanding of gene function and biological process but also for human medical improvement. Many computational methods have been proposed based on the similarity between all known disease genes (seed genes) and candidate genes in the entire gene interaction network. Under the hypothesis that potential disease-related genes should be near the seed genes in the network and only the seed genes that are located in the same module with the candidate genes will contribute to disease genes prediction, three modularized candidate disease gene prioritization algorithms (MCDGPAs) are proposed to identify disease-related genes. MCDGPA is divided into three steps: module partition, genes prioritization in each disease-associated module, and rank fusion for the global ranking. When applied to the prostate cancer and breast cancer network, MCDGPA significantly improves previous algorithms in terms of cross-validation and disease-related genes prediction. In addition, the improvement is robust to the selection of gene prioritization methods when implementing prioritization in each disease-associated module and module partition algorithms when implementing network partition. In this sense MCDGPA is a general framework that allows integrating many previous gene prioritization methods and improving predictive accuracy.
资助项目	National Natural Science of Foundation of China[10531070] ; National Natural Science of Foundation of China[10721101] ; National Natural Science of Foundation of China[KJCX-YW-S7]
WOS研究方向	Biotechnology & Applied Microbiology ; Genetics & Heredity
语种	英语
出版者	MARY ANN LIEBERT, INC
WOS记录号	WOS:000281137900002
内容类型	期刊论文
源URL	[http://ir.amss.ac.cn/handle/2S8OKBNM/10710]
专题	应用数学研究所
通讯作者	Chen, Xing
作者单位	1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Grad Univ, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Chen, Xing,Yan, Gui-Ying,Liao, Xiao-Ping. A Novel Candidate Disease Genes Prioritization Method Based on Module Partition and Rank Fusion[J]. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY,2010,14(4):337-356.
APA	Chen, Xing,Yan, Gui-Ying,&Liao, Xiao-Ping.(2010).A Novel Candidate Disease Genes Prioritization Method Based on Module Partition and Rank Fusion.OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY,14(4),337-356.
MLA	Chen, Xing,et al."A Novel Candidate Disease Genes Prioritization Method Based on Module Partition and Rank Fusion".OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 14.4(2010):337-356.