Computational Intelligence and Pattern Analysis in Biology Informatics (Wiley Series in Bioinformatics)

CONTENTS......Page 8
PREFACE......Page 12
CONTRIBUTORS......Page 18
PART I INTRODUCTION......Page 21
1.1 WHAT IS COMPUTATIONAL INTELLIGENCE?......Page 23
1.2.1 Artificia Neural Networks......Page 26
1.2.2 Fuzzy Logic......Page 29
1.2.3 Genetic and Evolutionary Computing Algorithms......Page 31
1.2.4 Probabilistic Computing and Belief Networks......Page 33
1.3 HYBRID INTELLIGENT SYSTEMS IN CI......Page 35
1.4.1 Swarm Intelligence......Page 37
1.4.2 Type-2 Fuzzy Sets......Page 41
1.4.3 Rough Set Theory......Page 42
1.4.5 Artificia Immune Systems......Page 44
1.4.7 The Differential Evolution Algorithm......Page 46
1.4.8 BFOA......Page 47
1.4.9 Bees Foraging Algorithm......Page 50
REFERENCES......Page 52
2.1 INTRODUCTION......Page 59
2.2.1 Template Matching......Page 60
2.3 FEATURE SELECTION......Page 61
2.3.1 Feature Selection in Measurement Space......Page 62
2.3.3 Soft Computing Approaches for Feature Selection......Page 67
2.4.1 Statistical Classifie......Page 68
2.5 UNSUPERVISED CLASSIFICATION OR CLUSTERING......Page 70
2.5.1 Definitio of Clustering and Proximity Measure......Page 71
2.5.2 Clustering Algorithms......Page 72
2.6 NEURAL NETWORK CLASSIFIER......Page 73
REFERENCES......Page 74
3.1 INTRODUCTION......Page 79
3.2 DATA......Page 80
3.3 TOOLS......Page 82
3.4 APPLICATIONS......Page 84
REFERENCES......Page 87
PART II SEQUENCE ANALYSIS......Page 91
4.1 INTRODUCTION......Page 93
4.2 RELATED LITERATURE /BACKGROUND......Page 94
4.3.1 Data Set......Page 97
-Gram Features......Page 98
4.3.3 Investigation of Promoter Recognition in Frequency Domain......Page 101
1......Page 105
4.4 CHALLENGES IN PROMOTER CLASSIFICATION 4.4.1 Limitations in the Neural Network Performance......Page 108
4.4.2 Genome-Wide Promoter Recognition......Page 110
4.5 CONCLUSIONS......Page 112
REFERENCES......Page 114
5.1 INTRODUCTION......Page 119
5.2 miRNA AND PROSTATE CANCER......Page 120
5.3 PREDICTION SOFTWARE FOR miRNAs......Page 121
5.4.1 Homology Evaluation......Page 123
5.4.2 Free Energy Computation......Page 124
5.5 PROPOSED METHOD......Page 125
5.6 AUTOMATIC PARAMETER TUNING......Page 126
5.7 EXPERIMENTAL ANALYSIS 5.7.1 Data Set Considered......Page 127
5.7.3 Results......Page 128
5.8 DISCUSSION AND CONCLUSIONS......Page 130
REFERENCES......Page 131
PART III STRUCTURE ANALYSIS......Page 137
6.1 INTRODUCTION......Page 139
6.2.1 RSmatch......Page 140
6.2.2 The RmotifDB System......Page 141
6.3.1 Search by Block......Page 144
6.3.2 Experiments and Results......Page 146
REFERENCES......Page 149
7.1 INTRODUCTION......Page 151
7.2.1 Kernels on Structured Data......Page 153
7.3 PROTEIN STRUCTURES 7.3.1 Background......Page 156
7.3.2 Algorithms for Structural Alignment and a Spectral Method......Page 157
7.3.3 Problem of Indels and Solution Using Neighborhoods......Page 159
7.4.1 Sequence-Based Kernels on Neighborhoods......Page 160
7.4.2 Structure-Based Kernels on Neighborhoods......Page 163
7.4.3 Combined Sequence and Structure-Based Kernels on Neighborhoods......Page 166
7.5.1 Neighborhood Pair Sum Kernels......Page 167
7.5.2 Alignment Kernels......Page 169
7.6.1 Experimental Setup......Page 171
7.6.2 Validation of Proposed Kernels......Page 172
7.6.4 Effects of Parameter Values......Page 178
7.7 DISCUSSION AND CONCLUSION......Page 182
APPENDIX A......Page 184
REFERENCES......Page 185
8.1 INTRODUCTION......Page 189
8.1.1 An Introduction to Protein Blocks......Page 190
8.1.2 An Introduction to Protein Kinases......Page 192
8.2.1 Insulin Receptor Kinase......Page 196
8.2.2 Mitogen-Activated Protein Kinase......Page 198
8.2.4 Cyclin-Dependant Kinase......Page 199
8.4 COMPARISON OF THE ACTIVE STATES OF HOMOLOGOUS KINASES......Page 200
8.5 CONCLUSIONS......Page 202
REFERENCES......Page 204
9.1 INTRODUCTION......Page 209
9.2.2 Graph Classificatio......Page 211
9.2.3 Kernel Functions......Page 212
9.2.4 Graph Database Mining......Page 213
9.3 RELATED WORKS......Page 214
9.3.1 Pattern Mining......Page 215
9.3.2 Vector-Based Classificatio......Page 216
9.3.3 Kernel Functions for Graph Classificatio......Page 217
9.4 ALIGNMENT KERNELS WITH PATTERN-BASED FEATURES......Page 218
9.4.1 Structure-Based Pattern Mining for Chemical Compound Classificatio......Page 219
9.4.2 Experimental Study......Page 221
9.5.1 Graph Pattern Diffusion Kernels for Accurate Graph Classificatio......Page 225
Theorem 9.5.1......Page 226
Proof:......Page 227
9.5.2 Experimental Study......Page 230
9.5.3 Conclusions......Page 234
Theorem 9.6.1......Page 236
Example 9.2......Page 237
9.6.2 Experimental Study......Page 241
9.7 GRAPH WAVELETS FOR TOPOLOGY COMPARISON......Page 243
9.7.1 Graph Wavelet Alignment Kernels for Drug Virtual Screening......Page 244
Definitio 9.7.1......Page 246
Example 9.3......Page 247
9.7.2 Experimental Study......Page 248
9.8 CONCLUSIONS......Page 252
REFERENCES......Page 253
10.1 INTRODUCTION......Page 257
10.2 PROPOSED METHODOLOGY......Page 258
10.2.1 Active Site Processing......Page 259
10.2.2 Genetic Algorithm-Based Ligand Design......Page 260
10.2.3 Postprocessing of the Ligand......Page 266
10.3.1 Comparative Analyses of the Contribution of Intramolecular and Intermolecular Energy for Ligand Designing......Page 267
10.3.2 Comparative Analyses of Interaction Energy and Hydrogen-Bond Interaction......Page 268
10.4 CONCLUSION......Page 273
REFERENCES......Page 275
PART IV MICROARRAY DATA ANALYSIS......Page 277
11.1 INTRODUCTION......Page 279
11.2.1 Clustering Algorithms......Page 280
11.2.2 Cluster Validity Indices......Page 282
11.3 DIFFERENTIAL EVOLUTION BASED FUZZY CLUSTERING 11.3.1 Vector Representation and Population Initialization......Page 283
11.3.5 Selection......Page 284
11.4.2 Performance Metrics......Page 285
11.4.3 Input Parameters......Page 286
11.4.4 Results......Page 287
11.5 INTEGRATED FUZZY CLUSTERING WITH SUPPORT VECTOR MACHINES 11.5.1 Support Vector Machines......Page 288
11.5.3 Results......Page 291
11.5.4 Biological Significanc......Page 292
REFERENCES......Page 295
12.1 INTRODUCTION......Page 297
12.2 MICROARRAY GENE EXPRESSION DATA......Page 298
12.3 SUPPORT VECTOR MACHINE CLASSIFIER......Page 299
12.4.2 Phase-II: Identificatio of Gene Markers......Page 301
12.5 DATA SETS AND PREPROCESSING......Page 302
12.5.2 Colon Cancer Data......Page 303
12.6.1 Classificatio Results......Page 304
12.6.2 Identificatio of Gene Markers......Page 305
12.7 DISCUSSION AND CONCLUSIONS......Page 309
REFERENCES......Page 310
13.1 INTRODUCTION......Page 313
13.3 PARPSBKM CLUSTERING IMPLEMENTATION......Page 314
13.4.1 Data Sets......Page 316
13.4.2 Timing Analysis......Page 317
13.4.3 Validity Analysis......Page 323
13.5 TEST FOR STATISTICAL SIGNIFICANCE......Page 324
REFERENCES......Page 325
PART V SYSTEMS BIOLOGY......Page 327
14.1 INTRODUCTION......Page 329
14.2 PRIORITIZATION BASED ON TEXT-MINING WITH REFERENCE TO PHENOTYPES......Page 331
14.3 PRIORITIZATION WITH NO DIRECT REFERENCE TO PHENOTYPES......Page 333
14.4 PRIORITIZATION USING INTERACTION NETWORKS......Page 334
14.5 PRIORITIZATION BASED ON JOINT USE OF INTERACTION NETWORK AND LITERATURE-BASED SIMILARITY BETWEEN PHENOTYPES......Page 337
14.6 FUSION OF DATA FROM MULTIPLE SOURCES......Page 340
REFERENCES......Page 342
15.2 BASIC DEFINITIONS [9, 10]......Page 345
15.4 CHARACTERISTICS OF PPIs......Page 347
15.5 DRIVING FORCES FOR THE FORMATION OF PPIs......Page 349
15.6.1 Experimental PPI Prediction Methodologies......Page 350
15.6.2 Computational PPI Prediction Methodologies......Page 353
APPENDIX I......Page 361
APPENDIX II......Page 362
REFERENCES......Page 364
16.1 INTRODUCTION......Page 369
16.2 TOPOLOGY OF PPI NETWORKS......Page 370
16.2.1 Topological Properties De nition 16.2.1 (Degree [15]):......Page 371
16.2.2 Topological Structures De nition 16.2.8 (Clique [21]):......Page 372
16.3 LITERATURE SURVEY......Page 373
16.4 PROBLEM DISCUSSION......Page 375
16.5 THEORETICAL ANALYSIS......Page 377
16.6 ALGORITHMIC APPROACH......Page 378
16.7 EMPIRICAL ANALYSIS......Page 380
16.7.1 Gene Ontology Studies from HPRD......Page 381
16.7.2 Study of Functional Enrichment with FatiGO......Page 383
16.8 CONCLUSIONS......Page 384
REFERENCES......Page 386