(44.192.10.166) 您好!臺灣時間:2021/03/06 19:25
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

我願授權國圖
: 
twitterline
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
相關次數:
  • 被引用被引用:0
  • 點閱點閱:244
  • 評分評分:系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔系統版面圖檔
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
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
孫光天、張家榮(民91)。機率因果神經網路於設因推論之研究。第七屆人工智慧與應用研討會,頁285-290,台中。
孫光天、張鈺玟(民90)。應用自我建構類神經網路於線上分析處理索引之研究。中華民國九十年全國計算機會議,pp. B145-152,台北。
孫光天、陳新豐(民88)。利用人工智慧技術於選題策略之研究。中國測驗學會測驗年刊,46(1),頁75-88。
金聚鈺(民94),遺傳程式規劃於B型肝炎 e 抗原轉態預測之研究。國立臺南大學碩士論文,台南市。
林怡君(民94),B型肝炎病毒序列於病毒學與臨床病程之研究。國立臺南大學碩士論文,台南市。
行政院衛生署(民93)。中華民國93年衛生統計動向。中華民國行政院衛生署。
行政院衛生署(民89)。中華民國85~89年台灣地區肝炎篩檢統計報告。中華民國行政院衛生署。
肝病防治學術基金會(民91),肝病防治會刊,台北,肝病防治學術基金會。
肝病防治學術基金會(民92),肝病防治會刊,台北,肝病防治學術基金會。
羅華強(民94),類神經網路-MATLAB的應用第七版,台北,高立出版。
葉怡成(民92),類神經網路模式應用與實作,台北,儒林出版。
Beasley, R. P. (1988). Hepatitis B virus. The major etiology of hepatocellular carcinoma. Cancer, 61: 1942-1956.
Blumberg, B. S., Alter, H. J., and Visnich, S. (1984). Landmark article Feb 15, 1965: A "new" antigen in leukemia sera. By Baruch S. Blumberg, Harvey J. Alter, and Sam. Visnich. JAMA, 252 (2): 252-257.
Chen, D. S. (1993) From hepatitis to hepatoma: lessons from type B viral hepatitis. Science, 262: 369-370.
Elizabeth, G., Violante, A., and Francisco, C. (1998). Using neural networks for differential diagnosis for Alzheimer disease and Vascular dementia. Expert Systems with Applications, 14: 219-225.
Fattovich, G. (2003). Natural history and prognosis of hepatitis B. Semin Liver Dis, 23 (1): 47-58.
Fausett, L. (1994). Fundamental of Neural Network. Prentice-Hall International.
Fernandez-Rodrigues, F., Gonzalez-Martel, C. and Sosvilla-Rivero, S. (2000). On the profitability of technical trading rules based on artificial neural networks: Evidence from the Madrid stock market. Economics Letters, 69: 89-94.
Ganem, D., and Varmus, H. E. (1987). The molecular biology of the hepatitis B viruses. Annu Rev Biochem, 56: 651-693.
Garcia-Perez, E., Violante, A., and Cervantes-Perez, F. (1998). Using neural networks for differential diagnosis for Alzheimer’s disease and Vascular dementia. Expert Systems with Applications, 14: 219-225.
Hiroshi, H., Yasuyuki, O., Hitomi, N., Seigo, T., Hiroyuki, T., and Hiroki, M. (1996). Application of neural network to the interpretation of laboratory data for the diagnosis of two forms of chronic active hepatitis. International Hepatology Communications, 5: 160-165.
Kane, M. (1995). Global programme for control of hepatitis B infection. Vaccine, 13 (suppl 1): S47-S49.
Kao, J. H., and Chen, D. S. (2002). Global control of hepatitis B virus infection. Lancet Infect Dis, 2 (7): 395-403.
Kao, J. H., Chen, P. J., Lai, M. Y., and Chen, D. S. (2003). Basal Core Promoter Mutations of Hepatitis B Virus Increase the Risk of Hepatocellular Carcinoma in Hepatitis B Carriers. Gastroenterology, 124 (2): 327-334.
Karin, K. L., Yuzo, M., and Alistair, H. K. (2002). Genetic variability in hepatitis B viruses. Journal of General Virology, 83: 1267–1280. Printed in Great Britain


Linder, R., Dew, D., Sudhoff, H., Theegarten, D., Remberger, K., Poppl, S. J., and Wagner, M. (2004) The ‘subsequent artificial neural network’ (SANN) approach might bring more classificatory power to ANN-based DNA micro-array analyses. Bioinformatics, 20 (18) 3544–3552.
Liu, C. J., Chen, P. J., Lai, M. Y., Kao, J. H., Chang, C. F., Wu, H. L., Shau, W. Y., and Chen, D. S. (2003). A Prospective Study Characterizing Full-Length Hepatitis B Virus Genomes During Acute Exacerbation. Gastroenterology, 124: 80–90.
Nakano, H., Okamoto, Y., Nakabayashi, H., Takamatsu, S., Tsujii, H., and Matsuoka, H. (1996). Application of neural network to the interpretation of laboratory data for the diagnosis of two forms of chronic active hepatitis. International Hepatology Communications, 5 (3): 160-165.
Negnevistsky, M. (2004). Artificial Intelligence: A Guide to Intelligent Systems (2nd ed.). England, Addison-Wesle.
Okamoto, H., Imai, M., Kametani, M., Nakamura, T., and Mayumi, M.. (1987). Genomic heterogeneity of hepatitis B virus in a 54-year-old woman who contracted the infection through materno-fetal transmission. Jpn J Exp Med, 57 (4): 231-236.
Pan, C. Q., and Zhang, J. X. (2005). Natural History and Clinical Consequences of Hepatitis B Virus Infection. International Journal of Medical Sciences 2 (1): 36-40.
Park, Y. R., Murray, T. J. and Chen, C. (1996). Predicting sun spots using a layered perceptron neural network. IEEE Transactions on Neural Networks, 7 (2): 501-505.
Stokes, D., and May, G. S. (2000). Real-time control of reactive ion etching using neural networks. IEEE Transactions on Semiconductor Manufacturing, 13 (4): 469-480.
Su, H. T., Bhat, N., Bhat, P., Minderman, A., and McAvoy, T. J. (1992). Integratingneural networks with first principles models for dynamic modeling. In 3rd IFAC Symposium on Dynamics and Control of Chemical Reactors, Distillation Columns and Batch Processes, USA, 77-82.
Sun, K. T., and Lai, Y. S. (2002). Applying Neural Network Technologies to Factor Analysis. Proceedings of the NSC, ROC – Part D: Mathematics, Science, and Technology Education, 12 (1): 19-30.
Murmis, V. G., Gisvold, J. J., Kinter, T. M., and Greenleaf, J. F. (1988). Texture analysis of ultrasound B-scans diagnosis of cancerous lesions in the breast. IEEE, Ultrasonics symposium 839-842.
Lo, J. Y., and Floyd, C. E. (1999). Application of artificial neural networks for diagnosis of breastcancer. IEEE, 3: 1755-1759.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top
系統版面圖檔 系統版面圖檔