Computational Intelligence in Bioinformatics (IEEE Press Series on Computational Intelligence)

Computational Intelligence in Bioinformatics......Page 4
Contents......Page 10
Preface......Page 14
Contributors......Page 20
Part One Gene Expression Analysis and Systems Biology......Page 24
1.1 Introduction......Page 26
1.2 Methods and Systems......Page 27
1.3 Experimental Results......Page 35
1.4 Conclusions......Page 40
References......Page 41
2.1 DNA Microarray Data Classification......Page 44
2.2 Evolutionary Approach to the Problem......Page 48
2.3 Gene Selection with Speciated Genetic Algorithm......Page 51
2.4 Cancer Classifiction Based on Ensemble Genetic Programming......Page 54
2.5 Conclusion......Page 60
References......Page 61
3.1 Introduction......Page 64
3.2 Related Work......Page 66
3.3 Evolutionary Clustering Algorithm......Page 67
3.4 Experimental Results......Page 74
3.5 Conclusions......Page 84
References......Page 86
4.1 Introduction......Page 90
4.2 Evolutionary Optimization......Page 93
4.3 Computational Network Modeling......Page 99
4.4 Extending Reach of Gene Networks......Page 108
4.5 Network Topology Analysis......Page 111
4.6 Summary......Page 113
References......Page 114
Part Two Sequence Analysis and Feature Detection......Page 120
5.1 Introduction......Page 122
5.2 Traditional Algorithms for Gene Selection......Page 123
5.3 New Fuzzy-Granular-Based Algorithm for Gene Selection......Page 125
5.4 Simulation......Page 130
5.5 Conclusions......Page 135
References......Page 136
6.1 Introduction......Page 140
6.2 Evolutionary Algorithms for Feature Selection......Page 142
6.3 Feature Selection for Clustering in Bioinformatics......Page 145
6.4 Feature Selection for Classification in Bioinformatics......Page 150
6.5 Frameworks and Data Sets......Page 156
References......Page 159
7.1 Introduction......Page 164
7.2 Methods......Page 167
7.3 Biological Significance......Page 181
7.4 Conclusions......Page 185
References......Page 186
Part Three Molecular Structure and Phylogenetics......Page 190
8.1 Introduction......Page 192
8.2 Biochemical Background......Page 193
8.3 The Docking Problem......Page 199
8.4 Protein–Ligand Docking Algorithms......Page 202
8.5 Evolutionary Algorithms......Page 205
8.6 Effect of Variation Operators......Page 208
8.7 Differential Evolution......Page 209
8.9 Comparison between Docking Methods......Page 212
8.10 Summary......Page 213
References......Page 214
9.1 Introduction......Page 220
9.2 Thermodynamic Models......Page 223
9.3 Methods......Page 229
9.4 Results......Page 232
9.5 Conclusion......Page 242
References......Page 244
10.1 Introduction......Page 248
10.2 Methods and Systems......Page 252
10.3 Results and Discussion......Page 254
10.4 Conclusions......Page 257
References......Page 258
11.1 Introduction......Page 260
11.2 Background in Phylogenetics......Page 261
11.3 Challenges and Opportunities for Evolutionary Computation......Page 267
11.4 One Contribution of Evolutionary Computation: Graphyl......Page 268
11.5 Some Other Contributions of Evolutionary computation......Page 279
11.6 Open Questions and Opportunities......Page 281
References......Page 283
Part Four Medicine......Page 286
12.1 Introduction......Page 288
12.2 Nature of Cancer......Page 289
12.3 Nature of Chemotherapy......Page 290
12.4 Models of Tumor Growth and Response......Page 291
12.5 Constraints on Chemotherapy......Page 293
12.6 Optimal Control Formulations of Cancer Chemotherapy......Page 294
12.7 Evolutionary Algorithms for Cancer Chemotherapy Optimization......Page 298
12.8 Encoding and Evaluation......Page 299
12.9 Applications of EAs to Chemotherapy Optimization Problems......Page 303
12.10 Related Work......Page 311
12.11 Oncology Workbench......Page 314
12.12 Conclusion......Page 316
References......Page 317
13.1 Introduction......Page 320
13.2 Brief Introduction to Ontologies......Page 326
13.3 Information Retrieval form Biological Text Documents: Related Work......Page 328
13.4 Ontology-Based IE and Knowledge Enhancement System......Page 332
13.5 Document Processor......Page 334
13.6 Biological Relation Extractor......Page 336
13.7 Relation-Based Query Answering......Page 341
13.8 Evaluation of the Biological Relation Extraction Process......Page 344
13.9 Biological Relation Characterizer......Page 346
13.10 Determining Strengths of Generic Biological Relations......Page 354
13.12 Conclusions and Future Work......Page 357
References......Page 359
Appendix Feasible Biological Relations......Page 361
Index......Page 364