Tak-Chung Lau. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2004. / Includes bibliographical references (leaves 190-198). / Abstracts in English and Chinese. / Abstract --- p.i / Acknowledgement --- p.iv / Chapter 1 --- Introduction --- p.1 / Chapter 1.1 --- Background Information --- p.4 / Chapter 1.2 --- Importance of the Problem --- p.6 / Chapter 1.3 --- Problem Definition and Proposed Algorithm Outline --- p.7 / Chapter 1.4 --- Simple Illustration --- p.10 / Chapter 1.5 --- Outline of the Thesis --- p.12 / Chapter 2 --- Survey --- p.14 / Chapter 2.1 --- Introduction --- p.14 / Chapter 2.2 --- Dynamic Programming (DP) --- p.15 / Chapter 2.2.1 --- Introduction --- p.15 / Chapter 2.2.2 --- Algorithm --- p.15 / Chapter 2.2.3 --- Example --- p.16 / Chapter 2.2.4 --- Complexity Analysis --- p.20 / Chapter 2.2.5 --- Summary --- p.21 / Chapter 2.3 --- General Alignment Tools --- p.21 / Chapter 2.4 --- K-Nearest Neighbor (KNN) --- p.22 / Chapter 2.4.1 --- Value of K --- p.22 / Chapter 2.4.2 --- Example --- p.23 / Chapter 2.4.3 --- Variations in KNN --- p.24 / Chapter 2.4.4 --- Summary --- p.24 / Chapter 2.5 --- Decision Tree --- p.25 / Chapter 2.5.1 --- General Information of Decision Tree --- p.25 / Chapter 2.5.2 --- Classification in Decision Tree --- p.26 / Chapter 2.5.3 --- Disadvantages in Decision Tree --- p.27 / Chapter 2.5.4 --- Comparison on Different Types of Trees --- p.28 / Chapter 2.5.5 --- Conclusion --- p.29 / Chapter 2.6 --- Hidden Markov Model (HMM) --- p.29 / Chapter 2.6.1 --- Markov Process --- p.29 / Chapter 2.6.2 --- Hidden Markov Model --- p.31 / Chapter 2.6.3 --- General Framework in HMM --- p.32 / Chapter 2.6.4 --- Example --- p.34 / Chapter 2.6.5 --- Drawbacks in HMM --- p.35 / Chapter 2.7 --- Chapter Summary --- p.36 / Chapter 3 --- Related Work --- p.37 / Chapter 3.1 --- Resonant Recognition Model (RRM) --- p.37 / Chapter 3.1.1 --- Introduction --- p.37 / Chapter 3.1.2 --- Encoding Stage --- p.39 / Chapter 3.1.3 --- Transformation Stage --- p.41 / Chapter 3.1.4 --- Evaluation Stage --- p.43 / Chapter 3.1.5 --- Important Conclusion in RRM --- p.47 / Chapter 3.1.6 --- Summary --- p.48 / Chapter 3.2 --- Motivation --- p.49 / Chapter 3.2.1 --- Example --- p.51 / Chapter 3.3 --- Chapter Summary --- p.53 / Chapter 4 --- Group Classification --- p.54 / Chapter 4.1 --- Introduction --- p.54 / Chapter 4.2 --- Design --- p.55 / Chapter 4.2.1 --- Data Preprocessing --- p.55 / Chapter 4.2.2 --- Encoding Stage --- p.58 / Chapter 4.2.3 --- Transformation Stage --- p.63 / Chapter 4.2.4 --- Evaluation Stage --- p.64 / Chapter 4.2.5 --- Classification --- p.72 / Chapter 4.2.6 --- Summary --- p.75 / Chapter 4.3 --- Experimental Settings --- p.75 / Chapter 4.3.1 --- "Statistics from Database of Secondary Structure in Pro- teins (DSSP) [27], [54]" --- p.76 / Chapter 4.3.2 --- Parameters Used --- p.77 / Chapter 4.3.3 --- Experimental Procedure --- p.79 / Chapter 4.4 --- Experimental Results --- p.79 / Chapter 4.4.1 --- Reference Group - Neurotoxin --- p.80 / Chapter 4.4.2 --- Reference Group - Biotin --- p.82 / Chapter 4.4.3 --- Average Results of all the Groups --- p.84 / Chapter 4.4.4 --- Conclusion in Experimental Results --- p.88 / Chapter 4.5 --- Discussion --- p.89 / Chapter 4.5.1 --- Discussion on the Experimental Results --- p.89 / Chapter 4.5.2 --- Complexity Analysis --- p.94 / Chapter 4.5.3 --- Other Discussion --- p.99 / Chapter 4.6 --- Chapter Summary --- p.102 / Chapter 5 --- Individual Classification --- p.103 / Chapter 5.1 --- Design --- p.103 / Chapter 5.1.1 --- Group Profile Generation --- p.104 / Chapter 5.1.2 --- Preparation of Each Testing Examples --- p.104 / Chapter 5.2 --- Design with Clustering --- p.104 / Chapter 5.2.1 --- Motivation --- p.105 / Chapter 5.2.2 --- Data Exception --- p.105 / Chapter 5.2.3 --- Clustering Technique --- p.110 / Chapter 5.2.4 --- Classification --- p.116 / Chapter 5.3 --- Hybridization of Our Approach and Sequence Alignment --- p.116 / Chapter 5.3.1 --- AlignRemove and AlignChange --- p.117 / Chapter 5.3.2 --- Classification --- p.119 / Chapter 5.4 --- Experimental Settings --- p.120 / Chapter 5.4.1 --- Parameters Used --- p.120 / Chapter 5.4.2 --- Choosing of Protein Functional Groups --- p.121 / Chapter 5.5 --- Experimental Results --- p.122 / Chapter 5.5.1 --- Experimental Results Setup --- p.122 / Chapter 5.5.2 --- Receiver Operating Characteristics (ROC) Curves --- p.123 / Chapter 5.5.3 --- Interpretation of Comparison Results --- p.125 / Chapter 5.5.4 --- Area under the Curve --- p.138 / Chapter 5.5.5 --- Classification with KNN --- p.141 / Chapter 5.5.6 --- Three Types of KNN --- p.142 / Chapter 5.5.7 --- Results in Three Types of KNN --- p.143 / Chapter 5.6 --- Complexity Analysis --- p.144 / Chapter 5.6.1 --- Complexity in Individual Classification --- p.144 / Chapter 5.6.2 --- Complexity in Individual Clustering Classification --- p.146 / Chapter 5.6.3 --- Complexity of Individual Classification in DP --- p.148 / Chapter 5.6.4 --- Conclusion --- p.148 / Chapter 5.7 --- Discussion --- p.149 / Chapter 5.7.1 --- Domain Expert Opinions --- p.149 / Chapter 5.7.2 --- Choosing the Threshold --- p.149 / Chapter 5.7.3 --- Statistical Support in an Individual Protein --- p.150 / Chapter 5.7.4 --- Discussion on Clustering --- p.151 / Chapter 5.7.5 --- Poor Performance in Hybridization --- p.154 / Chapter 5.8 --- Chapter Summary --- p.155 / Chapter 6 --- Application --- p.157 / Chapter 6.1 --- Introduction --- p.157 / Chapter 6.1.1 --- Construct the Correlation Graph --- p.157 / Chapter 6.1.2 --- Minimum Spanning Tree (MST) --- p.161 / Chapter 6.2 --- Application in Group Classification --- p.164 / Chapter 6.2.1 --- Groups with Weak Relationship --- p.164 / Chapter 6.2.2 --- Groups with Strong Relationship --- p.166 / Chapter 6.3 --- Application in Individual Classification --- p.168 / Chapter 6.4 --- Chapter Summary --- p.171 / Chapter 7 --- Discussion on Other Analysis --- p.172 / Chapter 7.1 --- Distanced MLN Encoding Scheme --- p.172 / Chapter 7.2 --- Unique Encoding Method --- p.174 / Chapter 7.3 --- Protein with Multiple Functions? --- p.175 / Chapter 7.4 --- Discussion on Sequence Similarity --- p.176 / Chapter 7.5 --- Functional Blocks in Proteins --- p.177 / Chapter 7.6 --- Issues in DSSP --- p.178 / Chapter 7.7 --- Flexible Encoding --- p.179 / Chapter 7.8 --- Advantages over Dynamic Programming --- p.179 / Chapter 7.9 --- Novel Research Direction --- p.180 / Chapter 8 --- Future Works --- p.182 / Chapter 8.1 --- Improvement in Encoding Scheme --- p.182 / Chapter 8.2 --- Analysis on Primary Protein Sequences --- p.183 / Chapter 8.3 --- In Between Spectrum Scaling --- p.184 / Chapter 8.4 --- Improvement in Hybridization --- p.185 / Chapter 8.5 --- Fuzzy Threshold Boundaries --- p.185 / Chapter 8.6 --- Optimal Parameters Setting --- p.186 / Chapter 8.7 --- Generalization Tool --- p.187 / Chapter 9 --- Conclusion --- p.188 / Bibliography --- p.190 / Chapter A --- Fourier Transform --- p.199 / Chapter A.1 --- Introduction --- p.199 / Chapter A.2 --- Example --- p.201 / Chapter A.3 --- Physical Meaning of Fourier Transform --- p.201
| Identifer | oai:union.ndltd.org:cuhk.edu.hk/oai:cuhk-dr:cuhk_324586 |
| Date | January 2004 |
| Contributors | Lau, Tak-Chung., Chinese University of Hong Kong Graduate School. Division of Computer Science and Engineering. |
| Source Sets | The Chinese University of Hong Kong |
| Language | English, Chinese |
| Detected Language | English |
| Type | Text, bibliography |
| Format | print, xvi, 202 leaves : ill. ; 30 cm. |
| Rights | Use of this resource is governed by the terms and conditions of the Creative Commons “Attribution-NonCommercial-NoDerivatives 4.0 International” License (http://creativecommons.org/licenses/by-nc-nd/4.0/) |
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