本論文針對各種生物資訊領域的應用分別提出四種不同的適應性聚類演算法。第一種是依據粗略集合的特性所衍生之特徵選取法(Rough-based Feature Selection)。此方法不需事先設定聚類類別的個數並可以偵測出接近實際中心的位置。本研究將其結合徑向基函數網路 (Radial Basis Function Neural Network, RBFNN) 並應用於癌症分類 (Cancer Classification)。第二個方法發展出一套相似基因發掘系統 (Similar Genes Discovery System, SGDS)，本研究應用Entrez Gene資料庫及基因本體論 (Gene Ontology) 上對基因功能的註解進行語意相似度的量測，進而群聚出功能相近或相似的基因。此外，更將其應用在已知有生物反應路徑的基因上，企圖預測出可能存在的其他生物反應路徑。第三個方法是以支援向量機(Support Vector Machine)為基礎結合相關係數特徵選取(Correlation Coefficient)所形成的癌症診斷系統。最後一個方法是應用在預測微陣列 (Microarray) 與表達序列標籤 (Expressed Sequence Tags, ESTs) 之關係的系統上。所提出的方法是依據網格為基礎的聚類演算法 (Grid-based Clustering)，利用網格空間來過濾不考靠的資料以提高預測的可靠性。本論文根據各演算法的應用，設計不同的實驗來驗證所提出方法及系統的效能。

The dissertation presents four adaptive clustering algorithms to solve various problems of bioinformatics applications. The first one is rough-based feature selection algorithm. It can find the relevant features without requiring the number of clusters to be known a priori and identify the centers which approximate to the correct ones. In this study, we propose a combination method of rough-based feature selection and RBF neural network for the classification of gene expression data. It has demonstrated that the approach is able to reduce the number of genes selected and to increase the classification accuracy rate. The second is Gene Ontology (GO) semantic similarity clustering. We implement a Similar Genes Discovery System (SGDS), based upon semantic similarity measure of GO and Entrez Gene, to identify groups of similar genes. It is our belief that expanding this concept to the well-known pathway, SGDS can discovery some candidate genes with interactions described in the literatures. The third is a diagnosis model which combines the correlation coefficient feature selection algorithm with support vector machines (SVM). A correlation function is employed as the criterion to measure the dependency between features and it can find the relevant features according to the orthogonal method by maximizing the overall dependency. The last algorithm, designed as the grid-based clustering algorithm, is proposed to detect outliers from pairs of gene expression data and expressed sequence tags (ESTs). We implement an intelligent system according to the radial basis function neural network (RBFNN) to learn their nonlinear relationship and predict the ESTs by input cDNA microarray data. The performances of the proposed algorithms are carefully verified by conducting various experiments with different applications.

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