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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/18199

Title: 以微陣列數據建立基因表現之強健動態模型
Robust Dynamic Models Estimation of Microarray Gene Expression Data
Authors: Meng-Lin Wu
Contributors: NTOU:Department of Communications Navigation and Control Engineering
Keywords: 微陣列;基因表現;基因演算法;奧坎濾波器;奇異值分解;特徵模式
Microarray;Gene expression;Genetic algorithm;Occam filter;Singular value decomposition;Characteristic mode
Date: 2005
Issue Date: 2011-07-04
Abstract: 細胞的生長是由一個母細胞分裂兩個子細胞。而這種週而復始的過程稱為細胞週期。許多疾病都是起因於細胞不正常的分裂,尤其是癌症。為了解細胞的複製與腫瘤的發生等與基因體不穩定性及細胞不正常分裂之相關性,細胞週期的控制機制和關鍵因子的研究極為迫切。由於細胞複製過程對疾病研究具有相當的重要性,微陣列技術的突破使得生物學家能夠準確測量到生物DNA的基因轉錄作用。首先,微陣列資料可能因為儀器解析度的不足、影像的污染、載片上的灰塵和刮痕及實驗上操作過程的錯誤,都可能使產生缺值。本論文根據固定秩數演算法及基因演算法,提出一個最佳化的微陣列資料缺值估測法則。然後,將使用徑向基底函數網路為微陣列資料的特徵模式進行建模。且在有限的微陣列實驗數據外,利用內插法增加了額外的時間點以提昇建模的準確性。更進一步,基於獨立隨機雜訊模型的假設,微陣列基因表現的特徵模式將用以重建無雜訊之微陣列資料。同時,我們將致力於利用無失真壓縮技術為主估測主要特徵模式,再以此主要特徵模式建構線性離散系統以進行動態建模,藉由此一系統的轉移矩陣可以輕易預測未來基因表現的強度。最後,本論文將使用酵母細胞週期的微陣列數據以驗證所提方法的實用性。
cells reproduce by duplicating their contents and then dividing into two. The repetition of this process is called the cell-cycle, and is the fundamental means by which all living creatures propagate. On the other hand, abnormal cell divisions are responsible for many diseases, most notably cancer. Therefore studying cell-cycle control mechanisms and the factors essential for the process is important in order to aid in our understanding of cell replication, malignancy, and reproductive diseases that are associated with genomic instability and abnormal cell divisions. Recent breakthroughs in microarray technology have enabled biologists to measure the number of transcripts made from every gene in an organism’s DNA. This microarray technology allows an unprecedented look at the state of a cell at a particular time within the cell-cycle. Due to the importance of understanding the cell duplication process, studies of transcriptional regulation during the cell-cycle of yeast were among the first experiments to be carried out using microarray technology. First, since microarray data sets often contain missing values due to various reasons, e.g. insufficient resolution, image corruption, dust or scratches on the slides, experimental error during the laboratory process or even robotic methods can create missing values, genetic algorithm are proposed to estimate the missing values in the microarray data. Then, applications of Radial Basis Function Neural Networks (RBF NN) to the characteristic modes modeling problem of gene expression will be investigated. Extra time points will be searched by interpolation such that the modeling ability of RBF NN can be improved. Furthermore, reconstruction of the observed microarray data matrix with random variables of Gaussian density of zero mean and known variance is explored. And the expression profiles for the multiple microarray data with the concerned effective characteristic modes will be reconstructed. Finally, the Occam filter which employs lossy data compression to separate signal from noise based on SVD, will be used to estimate the number of characteristic modes for reconstructing the noise-free microarray data and also modeling the characteristic modes by fitting of a linear time-discrete dynamical system. We can describe the time evolution of expression values by using a time translational matrix to predict future expression values based on their expression values at some initial time. The yeast cell-cycle data set is illustrated to show the validation of the proposed modeling methods.
URI: http://ethesys.lib.ntou.edu.tw/cdrfb3/record/#G0M93670016
Appears in Collections:[通訊與導航工程學系] 博碩士論文

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