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研究生： 王佩文
Pei-Wen Wang
論文名稱： 從年齡動態網路探討疾病表現與疾病盛行率
An Age dynamic network for human phenotypes and disease prevalence
指導教授： 吳立青
Li-Ching Wu
口試委員:
學位類別： 碩士
Master
系所名稱： 生醫理工學院 - 系統生物與生物資訊研究所
Graduate Institute of Systems Biology and Bioinformatics
畢業學年度： 97
語文別： 英文
論文頁數： 43
中文關鍵詞： 疾病網路 、疾病盛行率 、動態網路 、疾病組 、表現型
外文關鍵詞： dynamic network, disease network, disease prevalence, phenotype, diseaseome
相關次數： 點閱：11  下載：0 
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    • 社會網絡已被應用在基因、蛋白質及代謝等各個領域來解釋其致病機轉。在這裡，我們利用醫院獲得醫療數據及由Online Mendelian Inheritance in Man ( OMIM)中取得基因與病徵等相關資料，藉由網路分析方法建立一個年齡動態疾病表現網絡（Age dynamic phenotypic disease network, ADPDN ），來分析不同年齡層下疾病網路的差異性。由結果顯示，當病人得到某個疾病時，伴隨得到第二種疾病的機率隨著年齡的增加而增加，而性別的不同也會造成差異。透過網路年齡的動態網路分析，證明疾病表現與疾病盛行率在基因層次上亦發現因為年齡及性別而有所不同，此方法結合醫療及基因資訊，建立疾病與基因的關聯性，期能對研究此相關領域者有所幫助。

    Social networks have been investigated on genetic, proteomic, and metabolic fields as a viable path toward elucidating the origins of specific diseases. Here we use epidemiology view to summarizing correlations obtained from hospital dataset in an age dynamic phenotypic disease network (ADPDN). We show the evidence that the progression of disease connect by the links of the network is different for patients according to age and genders. Our study show that human phenotypes and disease prevalence can be demonstrated and by using network analysis, facilitating the potential to enhance our understanding of the origin and evolution of human diseases.

    Chinese Abstract i English Abstract ii List of figures v List of tables vi Chapter 1 Introduction 1 1-1 Background 1 1-2 Motivation 3 Chapter 2 Material and methods 5 2-1 Material 5 2-1-1 OMIM Morbid Map 5 2-1-2 ICD-9-CM codes 5 2-2 Methods 6 2-2-1 Network analysis 6 2-2-1-1 Degree and degree distribution 6 2-2-1-2 Clustering coefficient 7 2-2-1-3 Scale-free network 7 2-2-2 Statistical analysis 8 2-2-2-1 Hypothesis 8 2-2-2-2 McNemar’s test 8 2-2-2-3 Odds ratio 10 2-3 Data preprocessing 11 Chapter 3 Result 14 3-1 Hospital dataset 14 3-2 Combined hospital dataset and OMIM disease-gene association 16 3-3 Statistical data 17 3-4 Degree distribution 21 3-5 The age dynamic phenotypic disease network 23 3-6 Case study 28 Chapter 4 Discussion 38 References 41

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