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研究生： 李建邦
Chien-Pang Lee
論文名稱： 在微陣列資料中進行特徵選取與預測基因調控網路之研究
Feature Selection and Gene Regulatory Network Prediction on Microarray Data
指導教授： 呂永和
Yungho Leu
楊維寧
Wei-Ning Yang
口試委員: 鮑興國
Hsing-Kuo Pao
李瑞庭
Anthony J.T. Lee
葉耀明
Yao-Ming Yeh
學位類別： 博士
Doctor
系所名稱： 管理學院 - 資訊管理系
Department of Information Management
論文出版年： 2010
畢業學年度： 98
語文別： 英文
論文頁數： 87
中文關鍵詞： 動態時間校正 、特徵選取 、基因演算法 、基因調控網路 、微陣列資料分析 、粒子群最佳化 、卡方同質性檢定
外文關鍵詞： Dynamic time warping, Feature selection, Genetic algorithm, Gene regulatory networks, Microarray data analysis, Particle swarm optimization, χ2-test for homogeneity
相關次數： 點閱：410  下載：2 
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    • 由於生物學家希望能夠了解並偵測生物體內基因的表現情形，因此，有許多實驗技術陸續被發展應用於基因偵測的議題。近年來，由於微陣列技術不同於過去常被用來研究基因表現的傳統偵測技術，因此，微陣列技術已經成為研究基因表現的重要技術之一，例如：北方墨點法、南方墨點法、菌落雜交法及墨點分析等方法，在一次的實驗中只能偵測一個或少數基因的表現情形，但微陣列技術可以在一次的實驗中，同時偵測成千上萬基因的表現量，因此，微陣列技術大幅提升了研究基因表現的效率。而『特徵選取』與『預測基因調控網路』為微陣列資料分析中的兩個重要研究議題。本論文提出了兩個新的方法，並分別成功應用於『特徵選取』與『預測基因調控網路』兩個研究議題上。在『特徵選取』的部份，雖然許多方法已經被提出來進行微陣列資料的特徵選取，但這些方法大多數只能針對基因的重要性進行排序，無法直接建議應該挑選的基因數量。為了解決這個問題，我們提出了一個新的方法稱為genetic algorithm with dynamic parameter setting (簡稱GADP)，首先，我們使用GADP來產生大量的基因子集合，並根據每一個出現在基因子集合裡的頻率進行重要的排序，接著我們利用卡方分析中的同質性檢定(χ2-test for homogeneity)來決定應該挑選基因的數量。最後，為了驗證所挑選的基因是否真為重要的基因，我們使用支援向量機來進行樣本分類的判別。在本研究中，總共使用了六祖不同的微陣列資料，並藉由這些資料比較GADP與其他方法的效能，根據實驗結果顯示，GADP在挑選基因的數量及分類的正確率上的表現都比既有的方法為佳。
    在『預測基因調控網路』方面，由於許多預測基因調控網路的方法都忽略了基因之間的表現可能會存在時間延遲反應的問題，因此，本研究提出了一個新的預測基因調控網路的方法，我們將此方法稱為GA/PSO with DTW。GA/PSO with DTW使用動態時間校正(dynamic time warping)演算法來判斷任兩基因之間的表現是否存在時間延遲的問題。除此之外，我們使用粒子群最佳化(particle swarm optimization)演算法來找出可針對微陣列資料進行離散化的兩個門檻值。接著，根據離散化後的微陣列資料及基因表現的時間延遲反應判斷結果，GA/PSO with DTW利用基因演算法產生許多候選的基因調控網路，再從中挑選出最佳的基因調控網路。為了判斷GA/PSO with DTW的分析效果，我們使用兩組酵母菌資料的子調控網路來進行驗證。實驗結果顯示，GA/PSO with DTW在預測基因調控網路的敏感性及明確性都比其餘既有的方法還要好。

    In response to the need of the biologists to understand and measure the expression levels of genes in the living organisms, many technologies have been proposed. Among the proposed technologies, microarray technology is one of the most popular and powerful techniques for functional genomics study in the recent years. In contrast to the other existing technology such as northern blots, southern blots, colony hybridizations and dot blots which identify and measure only one gene or a few genes in an experiment, microarray technology can measure the expression levels of thousands of genes simultaneously in an experiment. Two important research issues of microarray data analysis are “Feature Selection” and “Gene Regulatory Network Prediction (GRN prediction)” from the microarray data. In this thesis, we propose novel methods for feature selection and constructing GRNs. In feature selection, although many methods have been proposed to select relevant genes, they only gave the ranks of importance of the genes. They did not suggest the number of genes needed for the analysis. We therefore propose a hybrid method, which is called genetic algorithm with dynamic parameter setting (GADP), to counter this problem. In this method, we first use GADP to generate a large number of gene subsets and rank the genes according to their occurrence frequencies in the gene subsets. Then, we use the χ2-test for homogeneity to select a proper number of the top-ranked genes for data analysis. Finally, we use a support vector machine (SVM) classifier to verify the efficiency of the selected genes. Six different microarray datasets are used to compare the performance of the GADP method with the other existing methods. The experimental results show that the GADP method is better than the existing methods in terms of number of selected genes and the classification accuracy.
    Although many methods have been proposed to construct GRNs, most of them ignored the time delay regulatory relation between genes. In this thesis, we propose a novel hybrid method, termed GA/PSO with DTW method, to construct GRNs from microarray data. The GA/PSO with DTW method uses the dynamic time warping (DTW) algorithm to determine whether a time delay relation exists between two genes. In addition, it uses the particle swarm optimization (PSO) algorithm to find two thresholds for discretizing the original microarray data. Based on the discretized data and the time delay relation among genes, the GA/PSO with DTW method uses a genetic algorithm to generate many candidate GRNs from which the predicted GRN is inferred. Two real-life sub-networks of yeast are used to verify the performance of the proposed method. The experimental results show that the GA/PSO with DTW method is better than the existing methods in terms of the prediction sensitivity and specificity.

    Chinese Abstract I Abstract III Acknowledgement V Table of Contents II List of Figures V List of Tables VII Chapter 1 Introduction 1 1.1 Background 1 1.1.1 Feature Selection 2 1.1.2 Gene Regulatory Network Prediction 3 1.2 Motivation 3 1.2.1 Feature Selection 3 1.2.2 Gene Regulatory Network Prediction 4 1.3 Contribution 5 1.3.1 Feature Selection 5 1.3.2 Gene Regulatory Network Prediction 6 1.4 Overview of the Dissertation 6 Chapter 2 An Introduction to Microarray 8 2.1 Microarray Experiment 8 2.2 Microarray Data 12 Chapter 3 Related Work 13 3.1 Literature Reviews of Feature Selection 13 3.2 Literature Reviews of GRN Prediction 14 3.2.1 Association rules 15 3.2.2 Bayesian network and Dynamic Bayesian network 15 3.2.3 Soft computing 15 3.3 Genetic Algorithm 16 3.4 Particle Swarm Optimization 22 3.5 Dynamic Time Warping 24 3.6 Support Vector Machine 28 3.7 K-Nearest Neighbors 29 3.8 χ2-Test for Homogeneity 30 Chapter 4 A Novel Hybrid Feature Selection Method for Microarray Data Analysis 32 4.1 Introduction 32 4.2 Microarray Dataset 34 4.3 Genetic Algorithms with Dynamic Parameter Setting 36 4.3.1 Initial Feature Selection 36 4.3.2 Genetic Algorithm with Dynamic Parameter Setting 37 4.3.3 Support Vector Machine 42 4.4 GADP vs. SGA 44 4.5 Experiments and Results 46 4.6 Summary 52 Chapter 5 Constructing Gene Regulatory Networks from Microarray Data Using GA/PSO with DTW 53 5.1 Introduction 53 5.2 Time-Series Microarray Data 55 5.3 GA/PSO with DTW 57 5.4 Experiment and Results 66 5.4.1 Datasets 66 5.4.2 Performance Measure 67 5.4.3 Analysis and Performance 67 5.5 Summary 72 Chapter 6 Conclusion 73 6.1 Feature Selection 73 6.2 Gene Regulatory Network Prediction 74 Bibliography 76

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