GA-based Feature Selection for Protein Secondary Structure Prediction｜國立臺灣科技大學博碩士論文系統

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研究生：	Fathurrochman Habibie Fathurrochman Habibie
論文名稱：	GA-based Feature Selection for Protein Secondary Structure Prediction GA-based Feature Selection for Protein Secondary Structure Prediction
指導教授：	呂永和 Yungho Leu
口試委員:	楊維寧 Wei-Ning Yang 陳雲岫 Yun-Shiow Chen
學位類別：	碩士 Master
系所名稱：	管理學院 - 資訊管理系 Department of Information Management
論文出版年：	2021
畢業學年度：	109
語文別：	英文
論文頁數：	87
中文關鍵詞：	Feature Selection 、Genetic Algorithm 、Protein Secondary Structure Prediction 、CNN 、BLSTM
外文關鍵詞：	Feature Selection, Genetic Algorithm, Protein Secondary Structure Prediction, CNN, BLSTM
相關次數：	點閱：231 下載：0
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Proteins are essential macromolecules for the structure and function of a cell. Interactions of proteins are responsible for controlling various vital functions in the body, such as having a role in activating the immune system, regulating oxygenation, and determining drug response. Usually, the secondary structure of a protein can be determined through experimental methods (e.g., X-ray crystallography, NMR). However, the experimental methods are very expensive, time-consuming, and require a complex procedure. Hence, the computational approach for predicting the secondary structure of a protein is important in the biology field.

The secondary structure of a protein can be determined by its constituent sequence of amino acids. Usually, the protein secondary structure prediction uses two fixed features: amino acid sequences and PSSM profiles. However, other additional protein features (e.g., biophysical, physicochemical, conformation scores) can improve protein secondary structure prediction accuracy. This thesis focuses on feature selection to improve the accuracy in predicting the secondary structure of a protein. In this thesis, we first proposed to use a CNN model and a genetic algorithm to find the optimal subset of features. Then we trained a CNN-BLSTM model using the selected features to archive 74.5% Q8 accuracy on the CB513 dataset.

Abstract  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    . . ..  ii
Acknowledgment  . . . . . . . . . . . . . . . . . . . . . . . . . . .     iii
Table of Contents  . . . . . . . . . . . . . . . . . . . . . . . . . . .     iii
List of Figures  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     vi
List of Tables  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .     viii
Chapter 1    Introduction      . . . . . . . . . . . . . . . . . . . . 1
1.    Background      . . . . . . . . . . . . . . . . . . . . . . . . . . . . .1
2.    Problem Formulation  . . . . . . . . . . . . . . . . . . . .  2
3.    Objectives   . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .  3
4.    Scope and Limitation  . . . . . . . . . . . . . . . . . . . .  3
5.    Contribution  . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
6.    Research Outline . . . . . . . . . . . . . . . . . . . . . . . .   4
Chapter 2    Related Work . . . . . . . . . . . . . . . . . . . . 6
Chapter 3    Theoretical Basis . . . . . . . . . . . . . . . . .10
1.    Protein . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    10
2.    Protein Structure . . . . . . . . . . . . . . . . . . . . . . . . . 10
3.    Protein Structure Prediction . . . . . . . . . . . . . . . 11
4.    Protein Features . . . . . . . . . . . . . . . . . . . . . . . . . . .13
4.1.    Amino Acid Sequences Features . . . . . . . . . . . 13
4.2.    Position-Specific Scoring Matrix (PSSM) . . . .14
4.3.    Physical Features . . . . . . . . . . . . . . . . . . . . . . . . . .14
4.4.    Conformation Parameters . . . . . . . . . . . . . . . . . .14
5.    Deep Learning Network . . . . . . . . . . . . . . . . . . . . . . . .15
5.1.    Convolutional Neural Network (CNN) . . . . . . 15
5.2.    Long Short Term Memory (LSTM) . . . . . . . . . . 16
5.3.    Bi-directional LSTM (BLSTM) . . . . . . . . . . . . . . . 18
5.4.    Attention Model . . . . . . . . . . . . . . . . . . . . . . . . . . .18
6.    Genetic Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Chapter 4    Proposed Method . . . . . . . . . . . . . . . . . . . . . 21
1.    System Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .21
2.    Dataset Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22
3.    System Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .   28
4.    GA-based Feature Selection . . . . . . . . . . . . . . . . . . . . 30
4.1.    Chromosome Representation     . . . . . . . . . . . . . . 30
4.2.    Fitness Function . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
4.3.    Tournament Selection . . . . . . . . . . . . . . . . . . . . .  33
4.4.    Crossover and Mutation . . . . . . . . . . . . . . . . . . . .34
5.    Architecture Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
5.1.    CNN Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . 36
5.2.    CNN-BLSTM Architecture . . . . . . . . . . . . . . . . . . .37
5.3.    CNN-BLSTM with Attentions Layer . . . . . . . . . . 38
5.4.    CNN-BLSTM with Highway Connections . . . . .38
6.    Training Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
7.    Evaluation Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . .   41
Chapter 5    Implementation . . . . . . . . . . . . . . . . . . . . . . . .43
1.    Implementation Environment . . . . . . . . . . . . . . . . . . . 43
2.    Data Preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . .44
3.    Architecture Implementation . . . . . . . . . . . . . . . . . . . .47
4.    CNN Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .48
4.1.    CNN-BLSTM Architecture . . . . . . . . . . . . . . . . . . . 48
4.2.    CNN-BLSTM with Attentions . . . . . . . . . . . . . . . . 49
4.3.    CNN-BLSTM with Highway . . . . . . . . . . . . . . . . . .50
4.4.    Genetic Algorithm Implementation . . . . . . . . . .51
5.    Evaluation Implementation . . . . . . . . . . . . . . . . . . . . . .52
Chapter 6    Experimental Result . . . . . . . . . . . . . . . . . . . . 56
1.    Dataset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .        56
2.    Finding Optimal Feature Subset . . . . . . . . . . . . . . . . .57
2.1.    Experimental Parameters . . . . . . . . . . . . . . . . . . . 57
2.2.    Single Objective GA . . . . . . . . . . . . . . . . . . . . . . . . 58
2.3.    Multiobjective GA . . . . . . . . . . . . . . . . . . . . . . . . . . 59
2.4.    Optimal Feature Subsets Result . . . . . . . . . . . . . 60
3.    Evaluation on Optimal Feature Subset . . . . . . . . . . . 61
4.    Comparison with previous studies     . . . . . . . . . . . . . . .63
Chapter 7    Conclusions     . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
1.    Conclusions     . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    65
2.    Future Works     . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
References     . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . 67
APPENDIX     . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .    71
A1.    Predicted Result on CASP10 T0711. . . . . . . . .71
A2.    Predicted Result on CASP10 T0651-D3. . . . .72
A3.    Predicted Result on CASP10 T0716. . . . . . . . 73
A4.    Predicted Result on CASP10 T0726-D2. . . . 74
A5.    Predicted Result on CASP10 T0685-D2. . . . 75


                                

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