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  5. 以支持向量機預測血清中蛋白與阿茲海默症之關係
 
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以支持向量機預測血清中蛋白與阿茲海默症之關係

Other Title
Prediction of Alzheimer’s disease with Selected Serum Proteins Using Support Vector Machine
Type
thesis
Date Issued
2014-07-21
Author(s)
陳建宇
Advisor
林詠峯
Subjects
系所名稱:醫學檢驗暨生物技術學系所
Description
學位別:碩士
語文別:中文
指導教授:林詠峯
共同指導教授:
口試委員:陳建和;廖辰中
中文關鍵字:支持向量機;阿茲海默症;亨丁頓氏症
Abstract
在全球已開發國家步入高齡化社會的同時,老人照護的問題日趨明顯,近年來各國政策都重視著老人照護的問題,在眾多的問題中阿茲海默症(Alzheimer's disease; AD)導致的失智症是最被關注的。阿茲海默症是一種漸進性的神經退化疾病,初期病徵並不明顯,其診斷相當依賴臨床人員的經驗,如使用MMSE scores。現今仍沒有能做確切診斷的生物標誌,故希望能從病人血清中特定蛋白質的差異尋找出有效診斷阿茲海默症的生物標誌,幫助疾病診斷、追蹤和治療。研究顯示神經退化疾病 ”亨丁頓舞蹈症(Huntington Disease; HD)” 的關聯蛋白參與AD相關的澱粉樣前驅蛋白(APP)的細胞內運輸,因此我們尋找這些相關蛋白在阿茲海默症的病患血清中是否有差異,並在這些差異中找出不以主觀診斷的確診阿茲海默症的方式。本實驗使用健康人與阿茲海默症患者的血清,以enzyme-linked immunosorbent assay (ELISA) 檢測 Jouberin (AHI-1)、動力蛋白輕中鏈 (DYNC1LI2)以及驅動蛋白輕鏈 (KLC2),發現與正常對照組與阿茲海默患者中有顯著的差異,再以支持向量機 (Support vector machine; SVM)建構出以血清中AHI-1、DYNC1LI2以及KLC2的表達量預測是否罹患阿茲海默症的生物標誌。實驗的結果顯示,大部分的數據除了年齡之外,在與其他數值統計的狀況之下,接收者操作特徵曲線(receiver operating characteristic curve;ROC Curve )的曲線下方的面積(Area under the Curve of ROC ;AUC)表現都比單項好。使用SVM統計生物標誌與MMSE Score的回歸分析統計中,DYNC1LI2是最佳的選擇。我們未來會更進一步建立可供對照的細胞模型,當這些蛋白過多或過少時會對細胞產生何種影響。
URI
https://203.71.86.71/handle/123456789/56906

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