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研究生:劉威良
研究生(外文):Wei-Liang Liu
論文名稱:B型肝炎e抗原轉態與病毒序列突變率預測之研究
論文名稱(外文):The Prediction of HBeAg Seroconversion and DNA Sequence Mutation Rate of HBV
指導教授:孫光天孫光天引用關係
指導教授(外文):Koun-Tem Sun
學位類別:碩士
校院名稱:國立臺南大學
系所名稱:資訊教育研究所碩士班
學門:教育學門
學類:教育科技學類
論文種類:學術論文
論文出版年:2006
畢業學年度:94
語文別:中文
論文頁數:59
中文關鍵詞:e抗原突變率類神經網路B型肝炎病毒分類預測
外文關鍵詞:HBeAgpredictionmutation ratehepatitis B virusclassificationartificial neural network
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B型肝炎病毒感染一直是台灣地區重要的公共衛生課題之一,因此本研究係以成功大學醫學院所提供之B型肝炎患者臨床資料為實驗樣本,並選擇對時間序列較常應用之類神經網路模型稍作改良,期望能以患者過去各月的樣本特徵,透過類神經網路推論,預測B型肝炎患者e抗原轉態及下個月B型肝炎病毒序列突變率,甚至能預測至更久的時間。經過本研究預測結果,在e抗原轉態預測,其敏感度、特異度及平均預測能力分別為74.44%、82.81%、81.73%。而以下個月病毒序列突變率為預測目標,其預測結果均方根誤差最小為0.00175,最大也僅有0.00277;而平均比例誤差最小為0.08449最大為0.32982。最後,以第六個月病毒序列突變率為預測目標,其均方根誤差方面,四位病患除了一位病患0.00381稍高外其餘皆在0.002以內;在平均比例誤差方面,最小為0.245442最大為0.781983。經由上述研究結果顯示,確實以時間序列為概念的類神經網路模型,在預測B型肝炎病患e抗原轉態及病毒序列突變率皆得到不錯的效果。
The infection of Hepatitis B Virus (HBV) has always been one of the most significant studies of public health in Taiwan. Thus, this research has taken the clinical information of HBV patients provided by College of Medicine National Cheng Kung University as experimental specimen. Also, a slightly modified model of Artificial Neural Network, which regularly is applied to time series, has been chosen for predicting HBeAg seroconversion of HBV patients, mutation rate of HBV DNA sequence of the next month, and even for making it possible to predict for a longer period of time through referencing Artificial Neural Network based on the patients’ specimen of the past months. The predicted result indicates that the sensitivity, specificity and average prediction ability of HBeAg seroconversion are 74.44%, 82.81% and 81.73%. Besides, if mutation rate of HBV DNA sequence of the next month is taken as a predicted goal, the predicted result shows that the minimum Root Mean Squared Error is 0.00175 and the maximum is only 0.00277; while the minimum Mean Relative Error is 0.08449 and the maximum is 0.32982. Finally, if mutation rate of HBV DNA sequence of the sixth month is taken as a predicted goal, only one out of the four patients is at 0.00381 in terms of Root Mean Squared Error, which is slightly higher, and the patients are below 0.002. The minimum Mean Relative Error is 0.245442 and the maximum is 0.781983. The above result shows that the model of Artificial Neural Network based on the concept of time series has verified its positive effect on predicting HBeAg seroconversion of HBV patients and mutation rate of HBV DNA sequence.
中文摘要 i
英文摘要 ii
誌謝 iii
目錄 iv
表目錄 v
圖目錄 vi
一、緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 名詞釋義 3
1.4 研究限制 4
二、文獻探討 5
2.1 B型肝炎簡介與e抗原轉態 5
2.2 B型肝炎病毒基因體與序列突變 6
2.3 類神經網路原理 7
2.4 倒傳遞類神經網路 11
2.5 類神經網路應用於醫學領域 16
三、研究方法 18
3.1 研究對象 18
3.2 研究分析處理流程 18
3.3 資料前處理 19
3.4 類神經網路程式發展 23
3.5 評估指標 29
四、研究結果 32
4.1 B型肝炎e抗原轉態結果 32
4.2 影響e抗原轉態之因素分析 33
4.3 B型肝炎病毒序列突變率內部測試結果 34
4.4 B型肝炎病毒序列突變率短期預測結果 40
4.5 B型肝炎病毒序列突變率長期預測結果 47
4.6 影響病毒序列突變率之因素分析 49
五、研究結論與建議 54
5.1 研究結果 54
5.2 後續研究建議 56
參考文獻 57
中文部份 57
英文部份 58
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