科學家藉由基因微陣列資料來瞭解數萬個基因所代表的意義與其相互關係，然而基因微陣列具有高維度、小樣本之特性，因此在分類時會造成許多困難。為了解決這些問題，目前已有學者相繼提出許多的基因選取法，但這些基因選取法大多未考慮到任何與疾病相關的基因可供基因選取時之參考。而近年來基因的研究技術不斷進步，在醫學、生物學上關於基因與疾病之間的相關資訊也開始陸續地被提出。本研究將修改反覆式貝氏模型平均法，並將致病基因的先驗資訊加入其中，以減少後續基因挑選之運算複雜度、提高預測精準度，並且為避免只有一組挑選過後的基因集而產生過度配適之問題，於是將多組挑選後的基因集之不確定性列入考慮，各基因集同時具有事後機率以利進行後續判斷。本研究之研究方法在基因挑選的機制上，將會分三階段進行：第一階段先將與致病基因功能性相似的基因去除；第二階段則針對留下的基因進行分群；第三階段則從各群集中選出代表性的基因做為候選基因，輔以致病基因進行基因挑選，同時將該次挑選後所留下的基因集與其事後機率記錄下來，重複執行直到達到我們想要的模型個數為止。不同於大多基因選取法最後只留下一組基因集，本研究會產生多組基因集與其事後機率以供後續判斷，並以四種特定疾病的資料檔進行基因挑選與分類預測。結果顯示了在這四個資料檔當中，兩個資料檔的分類正確率提升較為緩慢，另外兩個資料檔則是有顯著提升。

Gene selection and clustering techniques are usually applied for analyzing microarray data. However, most of them do not consider the risk genes presented in biological studies. Our proposed method will modify iterative Bayesian model averaging algorithm and consider the risk gene set as prior information for gene selection. One major advantage of our method is that it considers more than one model to avoid overfitting. The whole method includes three stages. The genes highly correlated with any risk gene are removed at the first stage, and the remaining genes are divided into clusters at the second stage. At the final stage, a representative gene is chosen from each cluster to form a candidate gene set. We then apply the modified iterative Bayesian model averaging algorithm to select the genes in the candidate set that are suitable for deriving a regression model with risk genes. This method is tested on four well-known gene expression data sets for breast cancer and prostate cancer. The experimental results show that our gene selection method outperforms or has similar prediction accuracy to the methods proposed by other studies.

中文

陳丁群 (2008)，以致病基因集為先驗資訊的基因選取方法之研究，國立成功大學資訊管理研究所碩士班碩士論文

張哲仁 (2008)，應用可自訂群數的非監督式學習法於基因選取，國立成功大學資訊管理研究所碩士班碩士論文

程中慧 (2006)，無歸納偏置影響因素的基因選取之研究，國立成功大學資訊管理研究所碩士班碩士論文

劉冠良 (2007)，以叢集分析與距離測度為基礎之基因選取法，國立成功大學資訊管理研究所碩士班碩士論文

鄭凱峰 (2004)，小樣本高維度資料中二階段分類法之效能評估－以基因微陣列資料癌症分類為例，國立成功大學工業與資訊管理學系碩士班碩士論文
 
英文

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