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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/54182

    Title: Regulatory genes prediction with microarray data and ontology
    Other Titles: 使用微陣列資料與本體論於基因調控關係預測
    Authors: 楊朝勛;Yang, Chao-Hsun
    Contributors: 淡江大學資訊工程學系博士班
    Keywords: 基因微陣列;調控基因預測;遺失值填補;動態時間配置法;基因本體論;Microarray Time-Series Data;Gene Regulation Prediction;Missing Value Imputation;Dynamic Time Warping;Gene Ontology
    Date: 2011
    Issue Date: 2011-06-16 22:07:42 (UTC+8)
    Abstract: 基因微陣列近年來被大量應用在生物相關研究上。生物學家可藉由基因微陣列實驗所得之大量實驗結果,來進行後續的研究與分析。然而,如何在大量微陣列實驗資料中找出具有調控關係的基因組,是微陣列資料分析中的一重要研究議題。現今由文獻中所提出之數種方法,皆有其限制與缺點。
    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 to impute these missing values are needed since many algorithms for microarray data analysis require a complete matrix of gene expression values. In addition, selecting informative genes from microarray gene expression data is essential while performing data analysis on these large amounts of data. To fit this need, a number of methods were proposed from various points of view. However, most existing methods have their limitations and disadvantages.
    In this dissertation, we propose a novel approach to predict potential regulatory gene pairs through our distance measurement that estimates the distances between gene pairs effectively. The distance measurement is based on the dynamic time warping (DTW) algorithm and the well-defined gene ontology (GO) structure for genes or proteins. GO contains definition (annotations) for genes that describe the biological meanings of them. The semantic distance of two genes within biological aspect can be measured by performing proper quantitative assessments of their corresponding GO annotations. Our distance measurement takes both DTW distances of expression values and GO semantic distances of gene pairs into consideration.
    Besides, we also propose a novel missing value imputation approach by combining our distance measurement with the k-nearest neighbor (KNN) method. Experimental results show that our missing value imputation approach outperforms other major methods in terms of the commonly-used assessment. After missing values in microarray time series raw data are estimated effectively with our imputation approach, we then perform our gene regulation prediction approach. According to experimental results, our approach can discover more known regulatory gene pairs compared with other methods. Researches on microarray time series data can hence be improved and facilitated with our approaches.
    Appears in Collections:[Graduate Institute & Department of Computer Science and Information Engineering] Thesis

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