A Network Flow Approach to Predict Drug Targets from Microarray Data

藉由生物晶片與網路流量方法來預測藥物標的

葉室亨(Shih-Heng Yeh)

指導教授 ： 蘇豐文

國立清華大學/電機資訊學院/資訊系統與應用研究所/碩士(2010年)

https://doi.org/10.6843/NTHU.2010.00478

摘要

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。

關鍵字

藥物標的 ； 網路流量 ； 攝護腺癌 ； 生物晶片

並列摘要

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.

並列關鍵字

Drug target ； Network flow ； Prostate cancer ； Microarray data

參考文獻

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