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研究生:陳韋仲
研究生(外文):Wei-Chung Chen
論文名稱:探勘一致性樣式間之時間相依性於基因-樣本-時間微陣列資料集
論文名稱(外文):Mining Temporally Dependent Coherent Patterns on Gene-Sample-Time Microarray Datasets
指導教授:曾新穆曾新穆引用關係辛致煒辛致煒引用關係
指導教授(外文):Shin-Mu TsengJyh-Wei Shin
學位類別:碩士
校院名稱:國立成功大學
系所名稱:資訊工程學系碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2009
畢業學年度:97
語文別:中文
論文頁數:75
中文關鍵詞:關連規則探勘基因表現值分析微陣列資料探勘
外文關鍵詞:Gene Expression AnalysisMicroarrayData MiningAssociation Rule Mining
相關次數:
  • 被引用被引用:0
  • 點閱點閱:145
  • 評分評分:
  • 下載下載:11
  • 收藏至我的研究室書目清單書目收藏:0
至目前為止,針對微陣列基因晶片進行資料探勘的分析中,大部分的研究都致力於叢集技術的方法上,然而僅藉由叢集技術並無法得知在這些基因之間的互動關係。雖然已有少數研究開始針對關連規則方向進行探討,但尚未有具體的架構應用於三維的基因-樣本-時間(Gene-Sample-Time)微陣列資料。因此,本篇論文針對三維的微陣列資料,提出一套探勘時序關連規則的方法。本方法主要可分為二個階段,第一階段為一致性樣式階段(Coherent Pattern Phase),主要針對三維的基因-樣本-時間微陣列資料,找出每個基因的表現值資料中所包含的頻繁一致性樣式,以代表基因的某個特定反應作用。第二階段為時間相依規則階段 (Temporal Dependency Rule Phase),主要由第一階段發掘的頻繁一致性樣式中,找出具有高度相關性和信賴度的頻繁一致性樣式,判斷頻繁一致性樣式之間的時間相依性,並組合可能存在的時序關連規則,以代表基因反應作用間的相互關係和時間相依關係。經由實驗分析證實,本篇論文所提出的方法,確實可以找出具有生物意義的時序關連規則,以提供生物學家有用之參考。
Microarray data analysis is a very popular topic in current studies on bioinformatics. Although the relevant analysis methods are focused on clustering mostly, the relations of genes cannot be generated purely by clustering mining. Some studies applied association rules mining on microarray data, but there is no concrete framework proposed for analyzing three-dimensional gene-sample-time microarray datasets. In this research, we proposed a temporal association rule mining method on three-dimensional microarray datasets. In the method, there are two main phases. In the first phase, namely coherent pattern phase, the frequently coherent patterns for all genes are discovered. In the second phase, namely temporal dependency rule phase, temporal association rules are explored by using the frequently coherent patterns with high correlation and confidence. The temporal association rules represent the regulated-relations between genes. Through experimental evaluation, our proposed method can discover meaningful temporal association rules which are helpful for biologists to find insightful biological knowledge.
ABSTRACT I
摘要 III
誌謝 IV
目錄 V
表目錄 VII
圖目錄 VIII
第一章 導論 1
1.1 背景 1
1.2 研究動機 2
1.3 研究目的 4
1.4 問題定義 4
1.4.1 頻繁一致性樣式 5
1.4.2 時間相依關係 7
1.5 研究方法 8
1.6 論文貢獻 10
1.7 論文架構 12
第二章 相關研究 13
2.1 相似度測量方法 13
2.1.1 距離測量方法 13
2.1.2 相關係數測量方法 14
2.1.3 其它測量方法 14
2.2 關連規則 18
2.2.1 關連規則定義 18
2.2.2 關連規則探勘方法 19
2.2.3 Apriori演算法 19
2.3 關連規則應用於微陣列資料 22
第三章 研究方法 24
3.1 方法概念 24
3.2 相似度計算 29
3.3 一致性樣式階段 32
3.3.1 探勘最短長度一致性樣式 32
3.3.2 合併一致性樣式 37
3.4 時間相依規則階段 39
3.4.1 組合候選項目集 39
3.4.2 發掘時序關連規則 41
第四章 實驗分析 45
4.1 實驗資料集 45
4.2 基因功能語意相似度計算方法 46
4.3 實驗結果 49
4.3.1 樣式相似度實驗分析 49
4.3.2 樣式最短長度實驗分析 53
4.3.3 時間延遲實驗分析 56
4.3.4 隨機規則實驗分析 60
4.3.5 規則重覆比例實驗分析 62
4.4 規則驗證 63
4.4.1 Cell Cycle反應路徑 64
4.4.2 Glycolysis反應路徑 65
4.4.3 相似度方法比較 66
4.5 實驗總結 67
第五章 結論與未來研究方向 69
5.1 結論 69
5.2 未來研究方向 69
參考文獻 71
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