微陣列晶片是發展及應用較為成熟的生物晶片技術，通常用來做為大量篩檢及平行分析的工具，可以應用在基因表現比較、基因序列分析、及新藥物開發等領域。本論文使用卵巢癌的微陣列樣本做為分析對象，提出適用於探索疾病基因表現量之流程，首先利用回歸分析粗略地挑出200個基因，再利用變異數分析檢定個別基因在不同樣本之表現差異，得到12個疾病基因標記，利用此12個基因表現量當作非監督式分群的輸入變數，比較哪種分群方法效果最好，並提供視覺化的圖形介面系統，可讓生醫學者更直觀且迅速的觀察基因表現之型態。
一個生物體的表現型態是經由一群特定基因或蛋白質透過複雜交互作用，結合時序及空間的變化共同完成的，而形成生物學家所熟悉生化調控路徑。本論文建立了一搜尋機制，從龐大的網路資訊中找出和輸入基因相關的調控資訊和基因，並建立由輸入基因為起始的調控路徑，配合微陣列的基因表現量資料庫找出各調控基因和各期癌症的相關性，可以找出形成腫瘤或是造成腫瘤惡化的關鍵調控基因。

Microarray is a mature technology of gene chip. It is a common tool for biological detection and parallel analysis and can be employed in comparison of gene expression, sequence analysis and development of new medicine etc. In this study, we consider ovarian cancer as the target for analysis and propose a procedure for analyzing gene expression in microarray data. At first, regression analysis is used to pick out 200 genes, and then ANOVA is employed to detect difference of gene expression in each sample to get 12 disease linked gene markers. These 12 gene expressions are considered as input variables of unsupervised classification methods to find the best effect of all methods. This paper also provides a graphic interface system for visualization. It can provide useful biological, diagnostic, and prognostic information for mechanistic studies. Specific genes or proteins are combined under mutual influence along time and space to archive the phenotype of an organism. Such a process is usually referred to as the biochemical pathways. In this paper, we construct a scheme of finding the related regulator gene and information with initial gene from Internet database to generate possible pathways. It can find the relations between regulator genes with each stage of cancer, and then get the critical regulator gene of causing tumor. Hopefully, such a scheme can provide researchers to use worldwide biological database to have clues on the research on microarray analysis.

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