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研究生:張宏卿
研究生(外文):Hung-Ching Chang
論文名稱:開發整合知識庫與資料建構基因集網路之方法
論文名稱(外文):Development of a knowledge-based and data-driven network construction method for gene sets
指導教授:蕭朱杏蕭朱杏引用關係
指導教授(外文):Chuhsing Kate Hsiao
口試委員:陳倩瑜陳卓逸盧子彬
口試委員(外文):Chien-Yu ChenCho-Yi ChenTzu-Pin Lu
口試日期:2019-06-06
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:流行病學與預防醫學研究所
學門:醫藥衛生學門
學類:公共衛生學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:61
中文關鍵詞:生物資料庫網路模型建構生物途徑基因網路節點度分佈
DOI:10.6342/NTU201901037
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網路模型建構已經被廣泛的應用在各個領域中,像是社群網路、生物傳導
路徑、神經網路等,其主要目的在於建構不同物件之間的關係。近年來,在生物醫學領域中,生物途徑分析的應用有越來越廣的趨勢,但是可能受限於目前已知的生物途徑數量,此趨勢逐漸趨於平緩。由於一個新的生物途徑必須經由許多實驗驗證,往往要耗費許多時間;若是能藉由網路模型建構方法來預測基因網路,並提供實驗端進行驗證,就可以加速研究的進程。然而,現有的網路模型建構方法是以資料驅動方法為主,所預測的結果並不一定都有高度的可信度。因此本研究提出「開發整合知識庫與資料建構基因集網路」的想法,試圖提高基因網路模型的可信度。方法的第一階段為網路結構之建構,著重於網路結構的分析,強調所預測的基因網路必須具備相關領域的網路特性,並透過知識庫所提供的資訊評估生成的網路結構的表現;而第二階段則是網路節點名稱之指定,藉由實際資料並透過互動式操作模式讓使用者能更加靈活的完成基因網路的建立。最後,我們將乳癌及卵巢癌的基因表達量資料應用在本研究之方法上,並預測兩種癌症的基因網路。
Network modeling aiming to discover the relationship among individual entities has been an active research topic in social network, biochemical reaction network and neural network. In biomedical area, pathway analysis of gene expression levels can be handled in the context of network as well. The pathway analysis has become a popular approach to analyzing gene expression profile, as it can summarize the complexity of gene relationship, reduce the number of genes and increase the explanatory power. However, this disease-pathway association analysis depends strongly on the accuracy of pathway structure and the availability of pathways, not to mention the challenging task in verifying a new pathway. It involves time-consuming laboratory work conducted in multiple research groups. Here, we propose a knowledge-based and data-driven approach to efficiently construct potential networks for a given set of genes, so that time and cost can be saved in discovering the pathway structure. A reference library of validated networks will be considered first and used to evaluate several network properties. This resulting information will next be utilized when developing the unknown network structure for a set of genes. Two cancer studies are considered to illustrate the proposed approach and to provide visualized results in predicting network structures and discovering relationship among genes.
目錄
致謝……………………………………………………………………………… i
中文摘要……………………………………………………………………… ii
Abstract…………………………………………………………………… iii

第一章 背景介紹及動機…………………………………………………… 1
第1.1 節 生物途徑分析近況………………………………………… 2
第1.2 節 基因網路建構方法簡介……………………………… 3

第二章 利用節點度生成網路結構………………………………… 6
第2.1 節 鄰接矩陣與節點資訊之參考網路前處理………………………… 6
第2.2 節 生成網路結構之演算法………………………………… 8
第2.2.1 節 生成節點度序列………………………………………… 9
第2.2.2 節 基於樣本節點度序列建構網路結構…………………… 13
第2.2.3 節 潛在網路結構之評分方法及排序………………………… 17

第三章 基於節點度及相關係數指定節點名稱…………………………… 20
第3.1 節 依節點度大小指定名稱………………………………………… 20
第3.2 節 依相關係數指定名稱…………………………………………… 21
第3.3 節 使用者自行指定名稱…………………………………………… 22

第四章 基因網路建構之應用………………………………………………… 26
第4.1 節 預測乳腺管內原位癌之基因網路……………………………… 26
第4.2 節 預測上皮性卵巢癌之基因網路…………………………………… 33

第五章 討論與展望…………………………………………………………… 35
第5.1 節 改良基於樣本節點度序列建構網路結構的成效……………… 35
第5.2 節 不同網路大小生成的潛在網路結構數量及其運算時間……… 36
第5.3 節 探討同構網路的數量………………………………………… 38
第5.4 節 參考網路來源的不確定性………………………………… 39
第5.5 節 樣本節點度序列的數量……………………………………………… 39
第5.6 節 網路結構的變異性………………………………………………… 40
第5.7 節 參考網路之選樣偏差……………………………………… 40
第5.8 節 探討同時使用知識庫及資料建構網路結構之可行性………… 41
第5.9 節 結論及未來展望………………………….......………………… 41

參考文獻…………………………………………………………………….….… 43
附錄……………………………………………………………………………...... 48
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