A Hybrid Approach to Estimate True Density Function for Gene Expression Data

Chapter · December 2010with7 Reads
DOI: 10.1007/978-3-642-24055-3_5
In book: Advances in Digital Image Processing and Information Technology, pp.44-54


    Accurate classification of diseases from microarray gene expression profile is a challenging task because of its high dimensional
    low sample data. Most of the gene selection methods discretize the continuous-valued gene expression data for estimating the
    marginal and joint probabilities that results in inherent error during discretization and reduces the classification accuracy.
    To overcome this difficulty, a hybrid fuzzy-rough set approach is proposed that generates a fuzzy equivalence class and constructs
    a fuzzy equivalence partition matrix to estimate the true density function for the continuous-valued gene expression data
    without discretization. The performance of the proposed approach is evaluated using six gene expression data. f-Information measure is used for gene selection and back propagation network is used for classification. Simulation results
    show that the proposed method estimate the true density function correctly without discretizing the continuous gene expression
    values. Further the proposed approach performs the integration required to computef-Information measure easily and results in highly informative genes that produces good classification accuracy.

    KeywordsGene Expression profiles–Fuzzy-Rough Set–
    f-Information–Back Propagation Network