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系統識別號 U0026-0812200915160472
論文名稱(中文) 利用系統生物學之分類與演算法分析癌症基因表現網路並預測人類癌症蛋白質交互作用
論文名稱(英文) Systems Biology Approach in Human Cancer Protein Interaction Prediction and Its Application to Cancer Gene Expression Network Analysis
校院名稱 成功大學
系所名稱(中) 資訊工程學系碩博士班
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 97
學期 2
出版年 98
研究生(中文) 李宗儒
研究生(英文) Tsung-Lu-Michael Lee
電子信箱 michael@iir.csie.ncku.edu.tw
學號 p7893108
學位類別 博士
語文別 英文
論文頁數 90頁
口試委員 口試委員-蔡正發
口試委員-蘇豐文
口試委員-劉校生
口試委員-曾新穆
口試委員-李宗南
指導教授-蔣榮先
口試委員-辛致煒
中文關鍵字 基因表現網路  分類  交互作用  蛋白質  生物資訊  系統生物學  癌症  演算法 
英文關鍵字 systems biology  cancer  classification  interaction  protein  SVM  algorithm  Fuzzy  bioinformatics  gene expression network 
學科別分類
中文摘要 本論文的主要目的,是為了提高人類癌症蛋白質交互作用的精確性,以及建立一個癌症引起的基因表現網路,並且分析在癌症中顯著的誘發生物路徑和生物功能間的交互關係。本論文的主要研究動機,是來自於Dr. Chinnaiyan和他在美國密西根大學病理學中心的研究同仁們所建立的 Integrative molecular concept modeling of prostate cancer progression,利用系統生物學以及生物資訊學的技術,來應用於癌症的研究與分析。結合了癌症生物微陣列晶片、蛋白質交互作用網路資料、以及生物註釋資料,本論文提出了一個電腦分類模型,Fuzzy-SVM Mixture Models,來提升高通量人類癌症蛋白質交互作用的精確性,並用以建立一個癌症引起的基因表現網路,並且推論在癌症中顯著的誘發生物路徑和生物功能間的交互關係。本論文的研究結果證明本論文方法有許多在分析大量不同癌症相關生物資料時的優點,並且利用網路系統生物方法推測出可能的蛋白質交互作用,同時視覺化癌症引起的基因表現網路。雖然解釋複雜的癌症造成的機制仍然是很大的挑戰,系統生物學的方法提供一個很好的開始以深入了解癌症的生物機制。
英文摘要 The objective of this dissertation is to enhance the quality of human cancer-associated protein interactions for constructing cancer-perturbed gene expression networks and interpreting their interconnection among significantly enriched cancer-perturbed pathways and functional annotations. Motivated by the research work of Dr. Chinnaiyan and his colleagues at the Michigan Center for Translational Pathology on building an integrative molecular concept modeling of prostate cancer progression, we applied systems biology and bioinfomatics strategies to the study of cancer disease. Combining cancer microarray, interactome and biological annotation data together, we propose a computational classification model, Fuzzy-SVM Mixture Model, to enhance the quality of high-throughput cancer-associated protein-protein interaction data sets for constructing cancer-perturbed gene expression networks and inferring their interconnections among significantly enriched pathways and functional annotations in cancer models. Our results demonstrate several advantages in analyzing large heterogeneous biological data sets associated with cancer disease, and we are able to speculate potential protein interactions and visualize cancer-perturbed gene expression networks by using network systems biology. Although the interpretation of the complex underlying mechanism in cancer disease remains a challenge, systems biology approach provides a good starting point to gain insight into cancer biology.
論文目次 1. INTRODUCTION 1
1.1 OVERVIEW 1
1.2 OBJECTIVE 4
1.3 ORGANIZATION OF DISSERTATION 5
2. WHY SYSTEMS BIOLOGY? 6
2.1 WHAT SYSTEMS CAN DO TO BIOLOGY? 8
2.1.1 Network Systems Biology 8
2.1.2 Systems Biology in Drug Discovery 9
3. IDENTIFICATION OF CANCER-ASSOCIATED PROTEIN-PROTEIN INTERACTIONS 11
3.1 FUZZY-SVM MIXTURE MODELS 12
3.1.1 Support Vector Machines 14
3.1.2 Feature Representation 15
3.1.3 Fuzzy Multisets classifier 17
3.2 EXPERIMENTAL RESULTS 23
3.2.1 Interaction Data 23
3.2.2 SVM Classifiers Evaluation 26
3.2.3 FM Classifiers Evaluation 27
3.2.4 FSMMs Evaluation 29
3.2.5 Comparison with Other Works 31
3.3 PREDICTION OF CANCER PPIS 33
3.3.1 Data Sources 33
3.3.2 Breast Cancer 34
3.3.3 Analysis 38
3.4 SUMMARY 38
4. INTERPRETATION OF CANCER-PERTURBED GENE EXPRESSION NETWORKS 39
4.1 BACKGROUND 40
4.2 PROPOSED MODELS 41
4.2.1 Our proposed systems biology model 41
4.2.2 Data sets 42
4.2.3 Functional Groups Association Networks 42
4.2.4 Pathway enrichment analysis 43
4.2.5 Visualization of Differentially Expressed Functions 46
4.2.6 Signaling Pathway mediated by PDGF 53
4.3 APPLICATIONS AND REMARKS 54
4.3.1 Cross-talks among Wnt, Notch and Shh pathways 54
4.3.2 Application in Epigenetics 60
5. CONCLUSION 64
5.1 CONCLUDING REMARKS 64
5.2 FUTURE WORK 65
5.2.1 Validation of Protein Interactions Predictions 65
5.2.2 Visualization 66
5.2.3 Interpretation and Collaboration 67
REFERENCES 68
APPENDIX 81
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