近年來，隨著微陣列技術快速的發展且廣泛應用，大量的生物醫學方面的文獻被發表並提供了大量有用的資訊。但是因為資訊量過大，而且作者們不是只針對疾病與基因之間的關係來做探討，亦或只觀察藥品影響基因表現值的變化，卻少將兩部分做結合來觀察藉此獲得新的關連性。因此許多藥品、疾病與基因之間的關連性被掩蓋\\而仍待被發掘。為了解決這樣的問題，許多的方法被提出，但是大部分的方法不是需要人的介入，就是需要額外的實驗去驗證找出結果。因此本論文的主要目標就是在於找出藏在於疾病與基因，以及相同基因與藥品之間所形成之隱藏連結關係。而首要條件是找出對於疾病有意義的基因。
當基因在對於觀察組(病患)比起對照組(正常人)有很明顯的特徵，藉此可以用以區別有疾病組跟正常組。為了將有疾病組跟正常組分類，許多的論文被發表來解決這樣的問題，但是大部分的方法在微陣列的特徵值的數目增加時，就變得非常沒有效率。為了提升分類的正確率以及加速現有分類演算法之速度，我們提出了微陣列特徵削減演算法(MARS)，來挑選出有鑑別度的基因。
實驗結果顯示，比其他的特徵選取結合多類別支援向量機的方法(MCSVM)，MARS結合相同MCSVM的方法的效能表現更為突出。而在比較整體分類效果的實驗中，MARS結合MCSVM的表現比現存論文的實驗結果更好。而且，在急性淋巴性白血病與急性骨髓性白血病(AML-ALL)資料組中，我們找出的22個基因中有19個在文獻中與AML與ALL疾病有關，3個基因是仍待文獻確定。因此更可以證明我們的方法不僅可以大量的削減不必要的特徵，更可以提高分類的正確性，而且所找的基因是跟疾病有重大相關性。
為了改善MARS選取臨界值的方式，我們提出一個新的疾病導向特徵選取演算法(DOFA)來找出對於疾病有關的基因。DOFA 利用基因演算法(genetic algorithm; GA)在選取基因的過程中自動的去選擇出有關的基因，再透過支援向量機與K最近鄰居法(K-nearest neighbor; KNN)來當分類器。實驗結果顯示針對於急性淋巴性白血病與急性骨髓性白血病資料組與 結腸癌資料組，DOFA分別找出了21個基因與25個基因。而其中21個基因中有20個跟AML與ALL疾病有直接相關性或者是跟癌症有相關性，1個基因是仍待文獻確定；而25個基因中有20個跟結腸癌有直接相關性或者是跟癌症有相關性，5個基因是仍待文獻確定。在特徵選取的方法比較實驗中，DOFA比起其他方法有更顯著的效果。在與其他不同的21個方法透過交叉驗證方法中的留一驗證(leave-one-out cross-validation; LOOCV)的方式進行實驗，DOFA的效能比起其他方式有更好的效果。再與其他5種不同的分類方法比較下，透過一對一(50%訓練，50%測試)的實驗方式的比較下，DOFA可以獲得比其他的方法更高的鑑別度。
在獲得對於疾病有意義的基因群之後，我們可以更進一步的透過這些基因去找出隱藏的連結。我們提出一個高明確度的基於微陣列基因表現資料之文獻探勘的方法用以找尋藏於疾病、藥品與基因之間的隱藏關係。這個方法能自動的從疾病或者藥品相關的微陣列資料中去選擇有相關的基因群，並且利用這些基因的名字與別稱去找出來相關的摘要集合。在獲得這些摘要集合之後，我們的所提的方法更進一步採用一個別稱拓展演算法(Alias Expansion Scheme)與權重評價方程式(Weight Function)，來將所收集到的摘要群進一步篩選掉不相關的摘要，並且更進一步地找出相關隱藏的關係。我們設計了三套場景來驗證我們所提出的演算法。在第一個場景之中的兩個實驗，我們分別找到15個以及11個隱藏關係。在第二個場景之中的兩個實驗，我們分別找到35個以及24個隱藏關係。而在第三個場景之中，我們找到9個隱藏關係。我們將第三個劇本與知名的軟體CoPub做比較，而我們所找到的隱藏關係並不存在於CoPub所找到的隱藏關係之中。因此可以證明我們的方法可以找出另外的隱藏關係。而透過ROC曲線的方式更能顯示出我們的方法不僅可以找出隱藏的關係，而且具有高明確性。
最後簡單地做個總結，透過MARS，我們可以在微陣列表現資料中找出與疾病高度相關的基因群。再進一步地用DOFA，我們加強了MARS選取基因的能力。最後我們提出一個高明確度的基於微陣列基因表現資料之文獻探勘的方法，結合了DOFA選取基因的能力，更進一步的透過這些基因去找出疾病與藥品間隱藏的連結。實驗結果證明，我們的方法不但可以找出隱藏於藥品與疾病之間的關係，更具有高明確性。

In recent years, with the microarray technique widely adopted, a large amount of biomedical literatures are published to provide a lot of useful information. However, some relationships among disease, genes and drug are still to be explored, since the authors only focus on part of the significant genes to the disease or the significant genes to the drug but not connect them to obtain new relationships. There are several methods proposed for finding out the hidden relationships, however many of them requires manual involvements. The main objective of this dissertation is to discover the hidden connections between human diseases and genes and the connections between drugs and the same genes. In order achieve this goal, the intermediate nodes (signification genes) must be found first. When a gene has more significant difference in observed group (abnormal patients) than in control group (normal persons), this gene is called significant genes to the disease. These signification genes often play a crucial role in cancer diagnosis and treatment. Via classifying the microarray gene expression data to find these significant genes, doctors can obtain the feasible and appropriate information for treatments that can give to the patients according to their cancer symptoms. A variety of existing classifiers have been proposed for this problem. However, most of them often work inefficiently when attributes grow up over thousands. To further improve the accuracy and the speed of the existing classifiers, a novel microarray attribute reduction scheme (MARS) is proposed for selecting significant genes to the disease.
Experimental results demonstrate that combining the proposed scheme with multiclass support vector machine (MCSVM) obtains better performance than other different gene selection methods with the same MCSVM. In addition, the proposed scheme with MCSVM performs better than the results listed in the existing literature.. Furthermore, 19 of 22 genes selected by the proposed scheme in acute lymphoblastic leukemia and acute myeloid leukemia (AML-ALL) dataset are related to the AML and ALL diseases that have been reported in the literatures. Thus the proposed scheme not only can significantly reduce large amount of attributes (genes) for gene expression classification problem, but also increase the classification accuracy.
MARS finds related gene set according to a threshold determined by using receiver operating characteristic (ROC) curve. However, it requires repeating the experiment many times to determine the best threshold. Hence, we propose a novel disease-oriented feature selection algorithm (DOFA) to improve MARS. DOFA uses the Genetic Algorithm (GA) in the selection method for automatic picking up the related genes and Support Vector Machine (SVM) and K-nearest-neighborhood (KNN) as the classifier. DOFA is tested on picking up related genes for AML-ALL and Colon datasets. For AML-ALL and Colon datasets, it selects 21 genes and 25 genes, respectively. Based on the literatures, it shows that 20 of 21 genes are related to the disease or cancers related for AML-ALL dataset and one of these genes is still uncertain. And 20 of 25 genes are directly related to the disease colon cancer or cancers related and 5 of these genes are still uncertain. Three more experiments are conducted to verify the discriminability of the genes selected by DOFA. Experimental results all indicate that DOFA obtains better performance than other competing methods. Thus DOFA not only can select the genes related to the diseases, but also increase the classification accuracy.
After obtaining the significant gene group, we can further use these genes to obtain the hidden connections. We propose a high specificity literature mining method based on microarray expression profile for discovering hidden connections among disease, drug, and genes. The proposed method can automatically select related genes from the disease or drug microarray expression profiles, and use the disease names or the drug names and gene names or aliases of the selected genes to obtain the related abstract collections. An alias expansion scheme and a weight function are used to eliminate the unrelated literatures. We perform three scenarios to verify the proposed method. Experimental results show that using the proposed method can obtain the hidden connections among diseases, genes and drugs. The (ROC) curve shows that the proposed method can not only find the hidden connections between diseases and drugs but also have high specificity.
Concluding this dissertation, our goal is to discover the hidden connections between the diseases and the drugs. In order to achieve this goal, we first proposed MARS to select the significant genes to the diseases. And then, we proposed DOFA to improve the ability of MARS. We proposed a high specificity literature mining method based on microarray expression profile for discovering the hidden connections among diseases, genes, and drugs. The proposed method combines the power of searching significant genes to the disease of DOFA to further obtain the hidden connections. Experimental results show that the proposed method not only can obtain the hidden connections among diseases, genes, and drugs, but also has high specificity.

1. Introduction 1
1.1 Motivations and objectives 1
1.2 Overview of the proposed literature mining method 2
1.3 Organization of this Dissertation 5
2. MARS: a microarray attribute reduction scheme for microarray
cancer classification problem 6
2.1 Background and Related Works 6
2.2 Microarray Attribute Reduction Scheme 12
2.2.1 Overview of MARS 12
2.2.2 Details of the Microarray Attribute Reduction Scheme 16
2.2.3 Multiclass Support Vector Machine 18
2.2.4 Receiver Operating Characteristic Analysis 20
2.3 Experimental Results 21
2.3.1 Comparison of the Gene Selection Methods 22
2.3.2 Experiment of Threshold Selection 27
2.3.3 Performances Comparison of Different Algorithms 36
2.4 Discussion 39
2.5 Summary 41
3. DOFA: A novel disease oriented gene selection algorithm 42
3.1 Background and Related Works 42
3.2 Disease-Oriented Feature Selection Algorithm 47
3.2.1 Overview of DOFA 48
3.2.2 Normalization Process 50
3.2.3 Gene Selection Process 51
3.2.3.1 Gene Reduction Phase 52
3.2.3.2 Classification Pattern Learning Phase 54
3.2.4 Classification Process 59
3.2.5 Fusion and Verification Process 59
3.3 Experimental Results 60
3.3.1 Experiments of the Disease Oriented Feature Selection Results 60
3.3.2 Comparisons of the Classification Results 62
3.4 Discussion 67
3.5 Summary 69
4. The Proposed High Specificity Literature Mining Method 71
4.1 Background and Related Works 71
4.2 The Proposed Method 77
4.2.1 Microarray Expression Profile Database Construction 80
4.2.2 Microarray Expression profile Selection 81
4.2.3 Gene Selection 82
4.2.4 Combination of Two Gene Sets 84
4.2.5 Generating Queries Related To the Disease Name and Genes 84
4.2.6 Fetching Disease Oriented Gene-related Abstracts 85
4.2.7 Fetching Drug Oriented Gene-related Abstracts 87
4.2.8 Fusion Part 87
4.3 Experimental Results 88
4.3.1 Scenario 1 88
4.3.2 Scenario 2 96
4.3.3 Scenario 3 100
4.4 Discussion 106
4.5 Summary 112
5. Conclusions and Future Works 114
References 117

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