“A Graph-based Method to Mine Coexpression Clusters Across Multiple Datasets,” Chinese Journal of Electronics, vol. 21, no. 4, pp. 657-662, 2012,
Citation: “A Graph-based Method to Mine Coexpression Clusters Across Multiple Datasets,” Chinese Journal of Electronics, vol. 21, no. 4, pp. 657-662, 2012,

A Graph-based Method to Mine Coexpression Clusters Across Multiple Datasets

Funds:  null
  • Received Date: 2011-09-01
  • Rev Recd Date: 2012-02-01
  • Publish Date: 2012-10-25
  • Mining coexpression clusters across multiple datasets is a major approach for identifying transcription modules in systems biology. The main difficulty of this problem lies in the fact that these subgraphs are buried among huge irrelevant connections. In this paper, we address this problem using a noise reduction strategy. It consists of three processes: (1) Coarse filtering; (2) Clustering potential subsets of graphs; (3) Refined filtering on those subsets. Using yeast as a model system, we demonstrate that most of the gene clusters derived from our method are enrichment clusters. That is they are likely to be functional homogenous entities or potential transcription modules.
  • loading
  • X.J. Zhou et al, “Functional annotation and network reconstructionthrough cross-platform integration of microarray data”,Nature Biotechnology, Vol.23, No.2, pp.238-243, 2003.
    X. Yan, X. Zhou and J. Han, “Mining closed relational graphswith connectivity constraints”, Proc. 2005 ACM SIGKDD Int.Conf. Knowledge Discovery in Databases, pp.324-333, 2005.
    H. Hu, X. Yan et al., “Mining coherent dense subgraphs acrossmassive biological networks for functional discovery”, BMCBioinformatics, Vol.21, pp.213-221, 2005.
    X. Yan, M. Mehan, Y. Huang, M.S. Waterman, P.S. Yu, X.J.Zhou, “A graph based approach to systematically reconstructhuman transcriptional regulatory modules”, Bioinformatics,Vol.23, No.13, pp.577-586, 2007.
    L. Chen, S.M. Wang, R.S. Chen, “A method to detect genecoexpression clusters from multiple microarrays”, Progress inBiochemistry and Biophysic, Vol.35, No.8, pp.914-920, 2008.
    F. Radicchi, C. Castellano, F. Cecconi et al., “Defining andidentifying communities in networks”, PNAS, Vol.101, No.9,pp.2658-2663, 2004.
    M. Girvan, M.E.J. Newman, “Community structure in socialand biological networks”, PNAS, Vol.99, No.12, pp.7821-7826,2002.
    H. Zhang, X. Zan et al., “Detecting dense subgraphs in complexnetworks based on edge density coefficient”, IEEE Fifth InternationalConference Bio-Inspired Computing: Theories andApplications (BIC-TA), pp.51-53, 2010.
    Q. Zheng, X.J. Wang, “GOEAST: a web-based software toolkitfor gene ontology enrichment analysis”, Nucleic Acids Res.,Vol.36, pp.358-363, 2008.
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (585) PDF downloads(863) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return