Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems

295 Pages·2005·1.37 MB·English

by  Edward Keedwell

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Intelligent
Bioinformatics
The application of artificial intelligence
techniques to bioinformatics problems
Edward Keedwell
and
Ajit Narayanan
School of Engineering, Computer Science and Mathematics
University of Exeter, UK
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Intelligent
Bioinformatics
i
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ii
JWBK023-FM JWBK023-Keedwell April5,2005 23:30 CharCount=0
Intelligent
Bioinformatics
The application of artificial intelligence
techniques to bioinformatics problems
Edward Keedwell
and
Ajit Narayanan
School of Engineering, Computer Science and Mathematics
University of Exeter, UK
iii
JWBK023-FM JWBK023-Keedwell April5,2005 23:30 CharCount=0
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Contents
Preface ix
Acknowledgement xi
PART 1 INTRODUCTION 1
1 Introduction to the Basics of Molecular Biology 3
1.1 Basiccellarchitecture 3
1.2 Thestructure,contentandscaleofdeoxyribonucleicacid(DNA) 4
1.3 Historyofthehumangenome 9
1.4 Genesandproteins 10
1.5 Currentknowledgeandthe‘centraldogma’ 21
1.6 Whyproteinsareimportant 23
1.7 Geneandcellregulation 24
1.8 Whencellregulationgoeswrong 26
1.9 So,whatisbioinformatics? 27
1.10 Summaryofchapter 28
1.11 Furtherreading 29
2 Introduction to Problems and Challenges
in Bioinformatics 31
2.1 Introduction 31
2.2 Genome 31
2.3 Transcriptome 40
2.4 Proteome 50
2.5 Interferencetechnology,virusesandtheimmunesystem 57
2.6 Summaryofchapter 63
2.7 Furtherreading 64
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vi CONTENTS
3 Introduction to Artificial Intelligence and
Computer Science 65
3.1 Introductiontosearch 65
3.2 Searchalgorithms 66
3.3 Heuristicsearchmethods 72
3.4 Optimalsearchstrategies 76
3.5 Problemswithsearchtechniques 83
3.6 Complexityofsearch 84
3.7 Useofgraphsinbioinformatics 86
3.8 Grammars,languagesandautomata 90
3.9 Classesofproblems 96
3.10 Summaryofchapter 98
3.11 Furtherreading 99
PART 2 CURRENT TECHNIQUES 101
4 Probabilistic Approaches 103
4.1 Introductiontoprobability 103
4.2 Bayes’Theorem 105
4.3 Bayesiannetworks 111
4.4 Markovnetworks 116
4.5 Summaryofchapter 125
4.6 References 126
5 Nearest Neighbour and Clustering Approaches 127
5.1 Introduction 127
5.2 Nearestneighbourmethod 130
5.3 Nearestneighbourapproachforsecondarystructureprotein
foldingprediction 132
5.4 Clustering 135
5.5 Advancedclusteringtechniques 138
5.6 Applicationguidelines 144
5.7 Summaryofchapter 145
5.8 References 146
6 Identification (Decision) Trees 147
6.1 Method 147
6.2 Gaincriterion 152
6.3 Overfittingandpruning 157
6.4 Applicationguidelines 160
6.5 Bioinformaticsapplications 163
6.6 Background 169
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CONTENTS vii
6.7 Summaryofchapter 170
6.8 References 170
7 Neural Networks 173
7.1 Method 173
7.2 Applicationguidelines 185
7.3 Bioinformaticsapplications 187
7.4 Background 192
7.5 Summaryofchapter 193
7.6 References 193
8 Genetic Algorithms 195
8.1 Single-objectivegeneticalgorithms–method 195
8.2 Single-objectivegeneticalgorithms–example 202
8.3 Multi-objectivegeneticalgorithms–method 205
8.4 Applicationguidelines 207
8.5 Geneticalgorithms–bioinformaticsapplications 210
8.6 Summaryofchapter 217
8.7 Referencesandfurtherreading 217
PART 3 FUTURE TECHNIQUES 219
9 Genetic Programming 221
9.1 Method 221
9.2 Applicationguidelines 230
9.3 Bioinformaticsapplications 232
9.4 Background 236
9.5 Summaryofchapter 236
9.6 References 237
10 Cellular Automata 239
10.1 Method 239
10.2 Applicationguidelines 245
10.3 Bioinformaticsapplications 247
10.4 Background 251
10.5 Summaryofchapter 252
10.6 Referencesandfurtherreading 252
11 Hybrid Methods 255
11.1 Method 255
11.2 Neural-geneticalgorithmforanalysinggeneexpressiondata 256
11.3 Geneticalgorithmandknearestneighbourhybridfor
biochemistrysolvation 262