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Detecting disease associated modules and prioritizing active genes based on high throughput data
Qiu, Yu-Qing; Zhang, Shihua1; Zhang, Xiang-Sun1; Chen, Luonan2,3
2010-01-13
Source PublicationBMC BIOINFORMATICS
ISSN1471-2105
Volume11Pages:12
AbstractBackground: The accumulation of high-throughput data greatly promotes computational investigation of gene function in the context of complex biological systems. However, a biological function is not simply controlled by an individual gene since genes function in a cooperative manner to achieve biological processes. In the study of human diseases, rather than to discover disease related genes, identifying disease associated pathways and modules becomes an essential problem in the field of systems biology. Results: In this paper, we propose a novel method to detect disease related gene modules or dysfunctional pathways based on global characteristics of interactome coupled with gene expression data. Specifically, we exploit interacting relationships between genes to define a gene's active score function based on the kernel trick, which can represent nonlinear effects of gene cooperativity. Then, modules or pathways are inferred based on the active scores evaluated by the support vector regression in a global and integrative manner. The efficiency and robustness of the proposed method are comprehensively validated by using both simulated and real data with the comparison to existing methods. Conclusions: By applying the proposed method to two cancer related problems, i. e. breast cancer and prostate cancer, we successfully identified active modules or dysfunctional pathways related to these two types of cancers with literature confirmed evidences. We show that this network-based method is highly efficient and can be applied to a large-scale problem especially for human disease related modules or pathway extraction. Moreover, this method can also be used for prioritizing genes associated with a specific phenotype or disease.
DOI10.1186/1471-2105-11-26
Language英语
Funding ProjectNational Natural Science Foundation of China[60873205] ; National Natural Science Foundation of China[10801131] ; Innovation Project of Chinese Academy of Sciences[kjcx-yw-s7] ; Ministry of Science and Technology, China[2006CB503905] ; Chief Scientist Program of Shanghai Institutes for Biological Sciences ; Chinese Academy of Sciences[2009CSP002] ; Scientific Research Foundation of Chinese Academy of Sciences ; Excellent PhD Thesis Award
WOS Research AreaBiochemistry & Molecular Biology ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS SubjectBiochemical Research Methods ; Biotechnology & Applied Microbiology ; Mathematical & Computational Biology
WOS IDWOS:000275199200001
PublisherBIOMED CENTRAL LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/182
Collection应用数学研究所
Corresponding AuthorZhang, Xiang-Sun
Affiliation1.Chinese Acad Sci, Acad Math & Syst Sci, Key Lab Random Complex Struct & Data Sci, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Shanghai Inst Biol Sci, SIBS Novo Nordisk Translat Res Ctr Prediabet, Key Lab Syst Biol, Shanghai 200031, Peoples R China
3.Osaka Sangyo Univ, Dept Elect Engn & Elect, Osaka 5748530, Japan
Recommended Citation
GB/T 7714
Qiu, Yu-Qing,Zhang, Shihua,Zhang, Xiang-Sun,et al. Detecting disease associated modules and prioritizing active genes based on high throughput data[J]. BMC BIOINFORMATICS,2010,11:12.
APA Qiu, Yu-Qing,Zhang, Shihua,Zhang, Xiang-Sun,&Chen, Luonan.(2010).Detecting disease associated modules and prioritizing active genes based on high throughput data.BMC BIOINFORMATICS,11,12.
MLA Qiu, Yu-Qing,et al."Detecting disease associated modules and prioritizing active genes based on high throughput data".BMC BIOINFORMATICS 11(2010):12.
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