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研究生:葉室亨
研究生(外文):Yeh, Shih-Heng
論文名稱:ANetworkFlowApproachtoPredictDrugTargetsfromMicroarrayData
論文名稱(外文):藉由生物晶片與網路流量方法來預測藥物標的
指導教授:蘇豐文蘇豐文引用關係
指導教授(外文):Soo, Von-Wun
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊系統與應用研究所
學門:電算機學門
學類:系統設計學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:英文
論文頁數:50
中文關鍵詞:藥物標的網路流量攝護腺癌生物晶片
外文關鍵詞:Drug targetNetwork flowProstate cancerMicroarray data
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在系統生物學中,具備系統方法的藥物發展是逐漸新起的領域,其目的是整合大規模的互動資料與實驗來闡述疾病的現象。在治療癌症的研究議題之中的藥物開發過程,尤其以如何發現與設計藥物標的為重要課題。然而,以往的藥物標的本身是一個試誤的臨床試驗,面對複合性疾病而言,將會花費較多時間與成本,而開發一個具有系統性的方法去預測可能的藥物標的組合成為一個具有挑戰性的任務。
本研究將針對設計藥物標的方法,提出新的系統架構,利用基於網路流量的方法下,去辨識出有效的藥物標的,以及呈現藥物標的之不同組合下,減少與其他不相關的組合比較次數的搜尋空間。我們使用攝護腺癌的生物晶片與DrugBank生物網站來做為我們的實驗測試來源與定義。藉由此方法,我們成功辨識出潛在的藥物標的具有非常強烈地與已知的攝護腺癌的治療藥物有所相關,並且發現更多其他潛在藥物標的與他們的藥物組合,是現今正吸引著生物學學家來探討。

System approach for medicine discovery is an emerging discipline in systems biology that aims at integrating large scale of the interaction data and experimental data to elucidate diseases. It also raises new issues in the drug discovery and design in development process for cancer treatment. However, drug target are still a trial-and-error experimental stage in clinical testing and it is a challenging task to develop a prediction model that can systematically detect the possible drug targets and their combinations to deal with a complex disease. We present a network flow-based approach to identify the effective drug targets and reduce the search space for drug target combination comparing with exhaustive search. We use the prostate cancer microarray data and DrugBank database as our test domain. We successfully identify potential drug targets which are strongly related to the well known drugs for prostate cancer treatment and also discover more potential drug targets and their combinations which attract the attention to biologists at present.
摘要.................................................................................................................................i
Abstraction………………………………………………………………………….....ii
1 Introduction 1
2 System Architecture 7
2.1 Network reconstruction from microarray data and protein-protein interactions database 8
2.2 The network flow approach 12
2.3 Filtering search space of drug target combination based on heuristic rules 20
3 results 22
3.1 Networks 23
3.2 Drug target discovery 25
3.3 The maximum flow and biological processes in recent prostate cancer drugs 31
3.4 The discovery of combination of drug targets 35
3.5 The effect of the partially-directed and directed graph in our method 39
3.6 The execution time of our methods 42
4 Conclusions 43
Acknowledgements 44
References 45
Appendix 50
? Appendix 1 : The all disease-related genes (include disease-causing genes) 50
? Appendix 2 :The flow and damage of drug targets 50
? Appendix 3 : Top 5% drug targets flow to each disease-related genes 50
? Appendix 4 : Prostate Cancer Drugs in the network appeared 50
Appendix 1 : All disease-related genes (include disease-causing genes) 1
Appendix 2 : The flow and damage of drug targets 2
Appendix 3 : Top 5% drug targets flow to each disease-related genes 1
Appendix 4 : Prostate Cancer Drugs in the network appeared 5


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