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研究生:劉郁彧
研究生(外文):Yu-Yu Liu
論文名稱:以胺基酸物理化學性質與小波分析預測甲型流感病毒H3N2亞型抗原性
論文名稱(外文):Predicting antigenicity of influenza virus A subtype H3N2 by using amino acid properties and wavelet analysis
指導教授:李國彬
指導教授(外文):Kuo-Bin Li
學位類別:碩士
校院名稱:國立陽明大學
系所名稱:生物醫學資訊研究所
學門:生命科學學門
學類:生物化學學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:英文
論文頁數:50
中文關鍵詞:抗原性物理化學性質小波轉換免疫優勢位置
外文關鍵詞:antigenicityphysicochemical propertywavelet transformimmunodominant site
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A型流感病毒由於經常藉由突變或胺基酸替換來躲過免疫防衛,目前是被視為最具威脅性的流感病毒之一。電腦運算模型常用來快速預測新爆發病毒株的抗原性,因此成為重要的研究課題。模型也可以對病毒株的疫苗設計提供進一步幫助。此研究中,我們提出一個整合胺基酸物理化學性質與小波轉換技術的方法,用來預測A型流感病毒的抗原性。我們的模型可以識別免疫優勢位置,包括抗原表現位,醣基化位置,分化支決定位。我們的模型在訓練集中791個H3N2配對抗原資料中於預測準確性,敏感度,特異度分別達到0.89,0.86,與0.91。在另外兩個獨立測試集H5N1與H1N1中,預測準確性,敏感度,特異度分別達到0.77,0.51,0.95與0.66,0.51,0.81. 此結果顯示本論文方法可以改善預測表現率,擷取重要的生物學訊息,並能夠辨識抗原優勢位置。
Type A influenza viruses are the most threatening influenza viruses to humans, because the virus can mutate or undergo amino acid substitutions frequently to evade the immune defense. Computational models that can rapidly indicate the antigenicity of new outbreak strains thus become useful. The models can also shed light on the development of new vaccine against the outbreak strain. In this study, we propose a novel method incorporating amino acid’s physicochemical properties and the wavelet transform technique to predict the antigenicity of hemagglutinin in influenza A virus. Our model is able to identify immunodominant sites such as epitope sites, glycosylation sites, clade sites. With a training set of 791 pairwise antigenic data of H3N2, our model achieves an accuracy, sensitivity and a specificity of 0.89, 0.86 and 0.91, respectively. With two independent validation sets, H5N1 and H1N1, the accuracy, sensitivity and specificity are 0.77, 0.51, 0.95 and 0.66, 0.51, 0.81 respectively. The results suggest that the proposed method can improve the prediction performance, extract important biological information, and detect immunodominant sites.
Table of Contents
誌謝 I
中文摘要 II
ABSTRACT III
TABLE OF CONTENTS IV
LIST OF FIGURES VI
LIST OF TABLES VII
CHAPTER 1 INTRODUCTION 1
CHAPTER 2 MATERIAL AND METHODS 4
2.1 THE ANTIGENIC DATA 4
2.2 CONVERT SEQUENCE INTO SIGNAL 7
2.3 THE WAVELET TRANSFORMATION 10
2.4 CONSTRUCTING FEATURE VECTORS 14
2.5 BUILDING AN SVM MODEL 15
2.6 DETERMINING IMMUNODOMINANT SITES 15
2.7 EVALUATING PREDICTION PERFORMANCE 17
CHAPTER 3 RESULTS 18
3.1 MODEL PERFORMANCE EVALUATION 18
3.2 IMMUNODOMINANT SITES 20
CHAPTER 4 DISCUSSION 23
4.1 THE AMINO ACID PROPERTIES 23
4.2 PARAMETER ESTIMATION 24
4.3 THE MACHINE LEARNING APPROACHES 28
CHAPTER 5 CONCLUSIONS 29
REFERENCES 30
APPENDIX PYTHON CODES FOR IMPLEMENTATION 34

LIST OF FIGURES
Figure 1 The Overview of the study 3
Figure 2 The antigenic data 5
Figure 3 The wavelet transform 12
Figure 4 The wavelet families 14
Figure 5 Immunodominant sites 21
Figure 6 The Clade diversity tree 22
Figure 7 Model Performance using different wavelet families 26

LIST OF TABLES

Table 1 139 H3N2 viral strain in all 791 antigenic pairwise data 5
Table 2 The five amino acid property indices used in this study 8
Table 3 The thirty-nine indices 9
Table 4 The model performance 19
Table 5 Five epitope regions 21
Table 6 Performance of all models in parameter estimation 27
Table 7 The performance of different machine learning approaches 28
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