Genomic protein functionality classification algorithms in frequency domain.

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