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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/55114

    Title: KNN-DTW Based Missing Value Imputation for Microarray Time Series Data
    Authors: Hsu, Hui-Huang;Yang, Andy C.;Lu, Ming-Da
    Contributors: 淡江大學資訊工程學系
    Keywords: Microarray Time Series Data;Missing Value Imputation;Dynamic Time Warping;K-Nearest Neighbor
    Date: 2011-03
    Issue Date: 2011-08-16 16:05:41 (UTC+8)
    Publisher: Oulu: Academy Publisher
    Abstract: Microarray technology provides an opportunity for scientists to analyze thousands of gene expression profiles simultaneously. However, microarray gene expression data often contain multiple missing expression values due to many reasons. Effective methods for missing value imputation in gene expression data are needed since many algorithms for gene analysis require a complete matrix of gene array values. Several algorithms are proposed to handle this problem, but they have various limitations. In this paper, we develop a novel method to impute missing values in microarray time-series data combining k-nearest neighbor (KNN) and dynamic time warping (DTW). We also analyze and implement several variants of DTW to further improve the efficiency and accuracy of our method. Experimental results show that our method is more accurate compared with existing missing value imputation methods on real microarray time series datasets.
    Relation: Journal of Computers 6(3), pp.418-425
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Journal Article

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