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研究生:湯鎰聰
研究生(外文):Yi-TsungTang
論文名稱:基因轉錄調控與蛋白質交互作用的擷取與預測之資料探勘方法研究
論文名稱(外文):Data Mining Approach in Extraction and Prediction of Gene Transcriptional Regulation and Protein Interaction
指導教授:高宏宇高宏宇引用關係
指導教授(外文):Hung-Yu Kao
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:98
中文關鍵詞:資料探勘非監督式學習半監督式學習基因轉錄調控蛋白質交互作用
外文關鍵詞:Data MiningUnsupervised LearningSemi-supervised LearningGene RegulationProtein-Protein Interaction
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隨著人類基因體的解碼在分子生物學上高產量的實驗,產生了大量基因與蛋白質交互作用相關的資訊。而在這大量的資訊中,系統生物學是一個藉由整合不同層面資訊以理解生物系統如何行使其功能的學術領域。透過對基因和蛋白質的研究,期望最終能夠建立整個生物系統的可理解模型。轉錄調控機制是生物系統中非常重要的部分。對於生物醫學家來說,了解基因在不同情況下的轉錄調控機制,亦是很重要且不容易解決的問題,要了解這些複雜且龐大的生物機制,主要的困難來自於基因調控系統與蛋白質交互作用的複雜性。在基因調控系統中,蛋白質與基因在不同外在條件下會有不同的調控關係。近年來,隨著生物實驗技術不斷的進步,許多不同因素下的基因轉錄調控機制陸續被發現並發表在生物醫學文獻中。隨著生物醫學資訊的快速發展,如何有效率地從大量的生物醫學文獻中擷取這些知識,已經是一個重要的議題。另外,對於蛋白質在轉錄調控機制中所扮演的角色和蛋白質間交互作用的機制,也是很重要且必須被解決的難題。然而,了解蛋白質間未知的交互作用,對於現今疾病的新藥物研發有非常密切的關係與重要性。所以,本論文提出了非監督式與半監督式系統來擷取生醫文獻中基因重要的轉錄調控資訊,也利用蛋白質交互作用網路中拓樸結構特性來預測未知的蛋白質交互作用關係。
本論文提出二個基因轉錄調控的擷取系統與二個蛋白質交互作用的預測系統來了解基因轉錄調控與蛋白質交互作用的機制。他們各是AutoPat,SSWPL,TransDomain和ATRP。擷取系統主要利用非監督式與半監督式架構來建立句型樣版,並用這些樣版來擷取生醫文獻中基因轉錄調控相關資訊。預測系統主要利用網路拓樸結構特性來建立交互作用樣版與模型,再利用這些樣版與模型來預測未知的蛋白質交互作用。透過生物醫學家的專業判別和現有已知生物實驗結果的交互評估與分析,可以成功的從文獻中擷取基因轉錄調控資訊的涵蓋率達70%,而依據文獻發表而記錄在蛋白質交互作用資料庫中的資料,當許多蛋白質的交互作用被發現,我們預測未知蛋白質交互作用的準確率也高達90%。愈來愈多的蛋白質交互作用被發現時,將有助於提升我們預測未知蛋白質交互作用的準確率。這顯示出這些方法可以有效且完整的擷取基因轉錄調控資訊,並準確的預測出未知的蛋白質交互作用,這對生物醫學研究或新藥研發都有很大的助益。
The successful decoding of human genome gives rise to flourishing research in molecular biology, thus creating abundant amount of information on the interactions among genes and proteins. Systems Biology is the academic domain to understand the function of biological mechanism by integrating information from different sources. The expected goal of Systems Biology is to understand and reconstruct the model of biological mechanism by studying the approaches of genes and proteins. Transcriptional regulation is a crucial component in biological mechanism. For scientists in the biomedical field, to understand the transcriptional regulation mechanism of genes under different conditions is a very huge task and remains to be solved. The main difficulty in understanding such complex and large biological mechanism lies in the complexity of the interactions between gene regulation system and proteins. In transcriptional regulation mechanism of genes, the relations between transcription factors and its target genes are usually changed by different biological conditions, such as the interaction among transcription factors.
Recently, many findings of transcriptional regulation mechanisms of genes are published in literature by improved biological experiments. Abundant information on gene regulation is contained in all these biomedical research papers, and with the rapid development in Biomedical Informatics, how to effectively extract this knowledge is becoming an important issue. The role of proteins in the gene transcriptional regulation mechanism, as well as the protein–protein interactions are also a complex mechanism that needs to be investigated. How to understand the unknown interactions between proteins is very important for that it can greatly contribute to the development of new drugs. In the research, we have proposed unsupervised and semi-supervised methods to extract the important gene transcriptional regulation information. We also use the characteristics of the network topology structure in protein interactions to predict the hidden connections of protein interactions.
In this research, we proposed two extraction methods for the transcriptional regulation mechanism of genes, AutoPat and SSWPL, and two prediction methods for protein–protein interactions mechanism, TransDomain and ATRP, to solve the above questions. For the extraction methods, we used the unsupervised and semi-supervised structure to build sentence patterns, and used these sentence patterns to extract the information on gene transcriptional regulation in biomedical research papers. The prediction methods mainly used network topology structure characteristics to build the protein domain patterns and prediction models for interactions, and used these templates and models to predict the unknown interactions between proteins. Through the cross-evaluation and analysis of knowledge possessed by researchers in the biomedical field and the known results of biological experiments, we can successfully extract information on gene transcriptional regulation with a recall rate of 70% from literature. Utilizing the protein–protein interactions that are published in literature can also achieve a precision rate of 90% in predicting the interactions among proteins while many interactions of well-studied proteins are discovered. The accuracy of our predicting method will be improved in predicting novel protein interactions while more and more protein interactions are identified. These results have shown that our models can successfully and effectively extract information on gene transcriptional regulation, and accurately predict the novel protein–protein interactions. This can be beneficial to the biomedical studies and to the development of new drugs in the future.
ABSTRACT I
中文摘要 IV
1. INTRODUCTION 1
1.1 Motivation 1
1.2 Preliminaries 3
1.3 Organization of the Dissertation 7
2. RELATED WORK 8
2.1 Biological Relation Extraction 8
2.2 Gene Transcriptional Regulation Extraction 10
2.3 Protein–Protein Interaction Prediction 13
3. EXTRACTION OF GENE TRANSCRIPTIONAL REGULATION 15
3.1 Introduction 15
3.2 Identification of Transcription Factors and Gene Names 18
3.3 The Pattern Generation Module 19
3.4 The information extraction module 21
3.5 Result and Discussion 23
3.5.1 Unsupervised Pattern Validation 23
3.5.2 Performance Evaluation 28
3.6 Summary 33
4. LEARNING PATTERNS FOR RELATION EXTRACTION 35
4.1 Introduction 35
4.2 Definitions of Relationship and Pattern of Gene Regulation 36
4.3 Generic Regulatory Pattern Generation Process 39
4.4 Relationship Extraction Process 40
4.5 Semi-supervised Weighted Pattern Learning Method 41
4.6 Result and Discussion 43
4.6.1 Evaluation to Select a New Training Set 44
4.6.2 Evaluation for Semi-supervised Weighted Pattern Learning Process 46
4.6.3 Evaluation of the Relationship Extraction Process 48
4.7 Summary 50
5. INFERENCE OF PROTEIN INTERACTIONS 51
5.1 Transitive Topology Approach 51
5.1.1 Strong Transitive Topology and Weak Transitive Topology 52
5.1.2 Transitive Role Feature Generation Step 54
5.1.3 Transitive Domain Pattern Generation Step 57
5.1.4 PPIs Prediction Step 57
5.1.5 Result and Discussion 60
5.2 Augmented Transitive Relationship Approach 66
5.2.1 Augmented Transitive Relationship 68
5.2.2 Augmented Transitive Features (ATF) and Predictor (ATRP) 68
5.2.3 ATRP with Direct PPI Distillation 71
5.2.4 Result and Discussion 74
5.3 Case Studies 81
5.4 Summary 83
6. CONCLUSION 84
REFERENCES 86
PUBLICATION 93
APPENDIX 94

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