Modeling and analysis of gene expression time-series based on co-expression

Carla S. Möller-Levet, Hujun Yin

    Research output: Contribution to journalArticlepeer-review


    In this paper a novel approach is introduced for modeling and clustering gene expression time-series. The radial basis function neural networks have been used to produce a generalized and smooth characterization of the expression time-series. A co-expression coefficient is defined to evaluate the similarities of the models based on their temporal shapes and the distribution of the time points. The profiles are grouped using a fuzzy clustering algorithm incorporated with the proposed co-expression coefficient metric. The results on artificial and real data are presented to illustrate the advantages of the metric and method in grouping temporal profiles. The proposed metric has also been compared with the commonly used correlation coefficient under the same procedures and the results show that the proposed method produces better biologicaly relevant clusters. © World Scientific Publishing Company.
    Original languageEnglish
    Pages (from-to)311-322
    Number of pages11
    JournalInternational Journal of Neural Systems
    Issue number4
    Publication statusPublished - Aug 2005


    • Co-expressed
    • Fuzzy clustering
    • Gene expressions
    • Neural networks
    • Similarity metric
    • Time-series


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