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研究生:劉又誠
研究生(外文):Yu-ChengLiu
論文名稱:微陣列資料中基因關係分析之關聯樣式探勘之研究
論文名稱(外文):A Study on Association Pattern Mining for Gene Relation Analysis in Microarray Datasets
指導教授:曾新穆曾新穆引用關係
指導教授(外文):Vincent S. Tseng
學位類別:博士
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:2012
畢業學年度:100
語文別:英文
論文頁數:142
中文關鍵詞:關聯樣式探勘關聯規則探勘頻繁樣式探勘基因表達分析
外文關鍵詞:association pattern miningassociation rule miningfrequent pattern mininggene expression analysis
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  • 下載下載:19
  • 收藏至我的研究室書目清單書目收藏:0
近年來關聯樣式 (Association pattern) 分析方已成為從基因表現資料(gene expression data)中探勘基因之間表達關聯性與說明細胞內部運作的重要方法。然而,仍然有不少生物學家感興趣的基因表現相關議題未被提出有效的方法來討論。在本論文當中,我們將針對其中三個議題來做討論。其中包含1)探索基因之間表達的間接關聯性,2)基於基因之間的關係強度來探索出更符合生物邏輯意義的基因作用關聯性,3)在不同實驗條件資料集之間探索出具有表現差異的基因作用關係。因此,本論文提出一系列以關聯樣式為基礎的演算法,用來解決生物學家於研究上的這些需求。
首先,為了探討基因之間表達的間接關聯性,我們提出以過濾方式為基礎之間接關聯規則探勘(Filtering-Based Indirect Association Rule Mining)演算法,簡稱FIARM,用以分析基因微陣列資料(gene microarray data)。於此研究中,我們以〈X, Y | M〉的樣式格式來表達基因X與Y之間透過M具有間接關係(indirect relation),型成一間接關聯規則。此探勘出來的規則其背後蘊含的生物意義代表基因X與基因M之間很有可能共同參與一個生物作用(biological activity),而基因Y與基因M之間很有可能共同參與另一個生物作用,並且基因M為此兩不同生物作用的共同必要因子(necessary factor)。因此,藉由探勘間接關聯規則的探勘,可協助生物學家從複雜的生物作用中釐清基因之間的作用關聯性,像是找出不同生物作用機制之間的生物標記(biomarker)與必要因子。在此研究中,我並們利用基因本體論(Gene Ontology)與在蛋白質互動網路(protein-protein interaction network)資料庫來驗證我們探勘出的基因關聯的正確性。從實驗結果呈現出,我們提出的FIARM演算法的確可以有效率的探索出不同於一般關聯樣式的基因作用間接關聯性,協助生物學家來進行基因相關研究。
接著,為了探討基於基因之間的關係強弱來探索出更符合生物邏輯意義的基因調控關聯性,我們提出一個以關聯性強度為基礎之多最小支持度關聯規則(Relational-Based Multiple Minimum Supports Association Rules)演算法,簡稱REMMAR。本演算法利用每一個基因對(gene pair)之間的調控關聯性強度(regulatory relation intensity)以調整其所屬最小關聯支持度,用來探勘出更具有生物意義(biological meaning)的重要關聯規則。於本研究的真實酵母菌(yeast)資料探討中,REMMAR利用任兩基因於酵母菌基因調控網路(gene regulatory network)中的最短路徑來當作關聯性強度的參考,從兩個酵母菌基因表現資料中分別探勘出關聯規則。從實驗結果顯示,REMMAR可以探勘出更多關聯性更強的規則,並且過濾掉在蛋白質互動網路(protein-protein interaction network)中不具有生物意義的關聯規則。接著,在利用文獻驗證所探勘出的關聯規則下,所提出的REMMAR演算法,相較於對照的Apriori演算法所得到的87.5%精確率(precision),能得到更高的100%精確率。因此,本研究所提出的REMMAR演算法能夠比傳統方法探勘出更具有生物意義的關聯規則,以協助生物學家進行複雜的基因探索。
最後,為了探討在不同實驗條件資料集之間探索出具有表現差異的基因作用關聯性,我們提出前k名具影響力的項目集探勘方法(Top-k Impactful Itemsets Miner) ,簡稱TIIM,只需要提供一個由使用者定義的參數k,就能探勘出在資料中的兩個不同實驗條件(condition)下,前k個最顯著差異的共同表現基因項目集(co-expression gene itemsets)。為了在基因之間賦與不同的權重,我們建立了一個影響程度表(impact degree table),用以記錄每一個基因於基因調控網路中,有多少個相鄰的基因在兩個不同實驗條件之間具有顯著表現差異,以當作權重的參考來源。最後,我們將分別利用文獻驗證及基因本體論豐富程度來分析,從兩個可公閞被取得、且具有兩個不同實驗條件特性的基因微陣列資料集中,被探勘出的前k名具影響力的項目集(top-k impactful itemsets)。在文獻驗證的結果中,所發現的共同表現基因項目集,相較於不考慮基因權重程度的對照方法所得的項目集,能得到更高的精確率。而在探勘出的項目集中所包含未被報導過的基因作用關係(gene interactionships),便能做為生物學家做為未來研究的方向。
總體而言,我們提出了數個不同應用目的基因表現資料集之關聯樣式分析演算法。透過不同資料集的效能驗證,我們所提出的演算法皆能有效的協助生物學家能夠於未來進一步的洞悉生物進程機制的研究參考。
Association pattern analysis methods are important techniques applied to gene expression data for efficiently finding expression relationships between genes and explaining the cell’s inner workings. However, there is a lack of effective methods for revealing gene relations in many research issues. In this dissertation, we addressed three important topics that are biologically interesting but have not been well explored: 1) discovering indirect correlations between genes, 2) exploring gene associations with strong biological meaning under consideration of the relation intensity between genes, and 3) revealing significantly differential gene expression relationships between two different conditions. A series of association pattern mining algorithms are proposed for these tasks.
First, in order to discover the indirect correlations between genes, the Filtering-based Indirect Association Rule Mining (FIARM) algorithm is proposed for analyzing gene microarray data. The form 〈 X, Y | M 〉 is used to present the indirect relation of X and Y that depends on M. This signifies that genes X and M are likely involved in a given biological activity. Furthermore, genes Y and M likely join to carry out another biological activity since gene M is the necessary factor for these biological activities. Such information can help biologists determine gene relationships in diverse biological activities, like exploring biomarkers and necessary factors between biological process mechanisms. In this research, the semantic similarity of Gene Ontology and the shortest distance in a protein-protein interaction network are used to verify the accuracy of discovered gene relations. Experimental evaluations show that the proposed method can discover indirect relationships dissimilated by association rules to effectively assist biologists in complicated genetic research.
Second, the RElational-based Multiple Minimum supports Association Rules (REMMAR) algorithm is proposed for exploring gene associations with strong biological meaning under consideration of relation intensity between genes. This method adjusts the minimum relation support for each gene pair depending on the regulatory relation intensity to discover more important association rules with strong biological meaning. In a case study of this research, REMMAR was utilized to find the shortest distance between any two genes in the yeast gene regulatory network as the relation intensity to discover the association rules from two yeast gene expression datasets. Experimental evaluations show that REMMAR can generate more rules with stronger relation intensity, and filter out rules without biological meaning in a protein-protein interaction network. Furthermore, the precision of the proposed method (100%) is higher than that (87.5%) of referenced Apriori method for discovering rules using a literature survey. Therefore, the proposed REMMAR algorithm can discover strong association rules in biological relationships dissimilated by traditional methods to assist biologists in complicated genetic explorations.
Finally, in order to discover the significantly differential gene expression relationships between two different conditions, a method named Top-k Impactful Itemsets Miner (TIIM), which only requires specifying a user-defined number k to explore top-k itemsets with most significantly differential co-expressed genes between two conditions, is proposed. To assign genes with different weights, a table with impact degrees for each gene is developed based on the number of neighboring genes, which are differently expressed in the dataset, on the gene regulatory network. The resultant top-k impactful itemsets are manually evaluated with previous literature and analyzed by a Gene Ontology enrichment method. Two publicly available time course microarray datasets are applied to the proposed method with two different experimental conditions. For both datasets, identified potential itemsets with co-expressed genes are evaluated with literature. The itemsets obtained here show higher precisions compared to that of the corresponding control when each gene is considered to have a constant impact degree. The several new gene interactions found in these itemsets are useful for biologists to gain insight into the mechanisms underpinning biological processes.
In summary, a set of association pattern mining algorithms are proposed to satisfy biologists with different purposes for analyzing gene expression datasets. Various real datasets are applied to the proposed methods to verify their effectiveness in helping biologists to gain insight into the underlying mechanisms of certain biological or clinical processes.
摘要…………………………………………………………………………………I
ABSTRACT……………………………………………………………………………………………III
誌謝…………………………………………………………………………………V
CONTENTS……………………………………………………………………………………………VI
LIST OF FIGURES………………………………………………………………………………VIII
LIST OF TABLES…………………………………………………………………………………X
CHAPTER 1 INTRODUCTION………………………………………………………………1
1.1 MOTIVATION………………………………………………………………………………………1
1.2 OVERVIEW OF DISSERTATION……………………………………………………4
1.2.1 DISCOVERING INDIRECT GENE ASSOCIATIONS BY FILTERING-BASED INDIRECT ASSOCIATION RULE MINING…………………………4
1.2.2 DISCOVERING REGULATION-INTENSIFIED GENE ASSOCIATIONS BY RELATIONAL-BASED ASSOCIATION RULE MINING……………………………………………………………………………………………5
1.2.3 MINING TOP-K DIFFERENTIAL CO-EXPRESSION PATTERNS FROM COMPARATIVE GENE EXPRESSION DATASETS……………………………………………6
1.3 ORGANIZATION OF DISSERTATION………………………………………………6
CHAPTER 2 BACKGROUND AND RELATED WORK…………………………………8
2.1 ASSOCIATION PATTERN MINING…………………………………………………8
2.2 INDIRECT ASSOCIATION RULES……………………………………………9
2.3 GENE ONTOLOGY……………………………………………………………………………………………10
CHAPTER 3 DISCOVERING INDIRECT GENE ASSOCIATIONS BY FILTERING-BASED INDIRECT ASSOCIATION RULE MINING…………………………………………………………12
3.1 INTRODUCTION……………………………………………………………………………………………12
3.2 PROPOSED METHOD………………………………………………………………………………………13
3.2.1 TRANSFORMATION OF GENE EXPRESSION DATA……………………………14
3.2.2 MINING OF INDIRECT ASSOCIATION RULES USING FIARM…14
3.3 RESULTS AND DISCUSSION……………………………………………………16
3.3.1 EXPERIMENTAL SETUP………………………………………………………16
3.3.2 VERIFICATION WITH GO SEMANTIC SIMILARITY………………………17
3.3.3 VERIFICATION WITH SHORTEST DISTANCE OF RULES IN PPIN……………………………………………………………………20
3.3.4 EVALUATION WITHOUT FIARM-MEASURE CONDITION…………………20
3.3.5 EVALUATION WITH FIARM-MEASURE CONDITION…………………………30
3.4 SUMMARY……………………………………………………………………………………………32
CHAPTER 4 DISCOVERING REGULATION-INTENSIFIED GENE ASSOCIATIONS BY RELATIONAL-BASED ASSOCIATION RULE MINING……………………………………………………………………………………………34
4.1 INTRODUCTION……………………………………………………………………………………………34
4.2 PROPOSED METHOD………………………………………………………………………………35
4.2.1 TRANSFORMATION OF GENE EXPRESSION DATA……………………36
4.2.2 BASIC DEFINITIONS………………………………………………37
4.2.3 RELATIONAL-BASED MULTIPLE MINIMUM SUPPORTS ASSOCIATION RULE (REMMAR) MINING…………………………………………………40
4.3 RESULTS AND DISCUSSION……………………………………………………………43
4.3.1 DATASETS……………………………………………………………………………………………43
4.3.2 EVALUATION WITH PPIN……………………………………………………………44
4.3.3 EVALUATION WITH LITERATURE………………………………………………47
4.4 SUMMARY……………………………………………………………………………………………50
CHAPTER 5 MINING TOP-K DIFFERENTIAL CO-EXPRESSION PATTERNS FROM COMPARATIVE GENE EXPRESSION DATASETS…………………………………………51
5.1 INTRODUCTION……………………………………………………………………………………………51
5.2 PROPOSED METHOD………………………………………………………………………………………52
5.2.1 GENE EXPRESSION DATA TRANSFORMATION………………………………53
5.2.2 BUILDING AN IMPACT DEGREE TABLE……………………………………………56
5.2.3 BASIC DEFINITIONS………………………………………………57
5.2.4 TOP-K IMPACTFUL ITEMSETS MINER (TIIM)…………………………59
5.3 RESULTS AND DISCUSSION………………………………………………………62
5.3.1 DATASETS……………………………………………………………………………………………63
5.3.2 EVALUATION WITH LITERATURE………………………………………………64
5.3.3 GO ENRICHMENT ANALYSIS…………………………………………………………67
5.4 SUMMARY……………………………………………………………………………………………70
CHAPTER 6 CONCLUSION AND FUTURE WORK…………………………………………………71
BIBLIOGRAPHY……………………………………………………………………………………………74
APPENDIX A……………………………………………………………………………………………87
APPENDIX B……………………………………………………………………………………………93
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