Intelligent Bioinformatics

Edward Keedwell is an Associate Professor in Computer Science. He joined the Computer Science discipline in 2006 having previously been a Research Fellow in the Centre for Water Systems and was appointed as a lecturer in Computer Science in 2009.Ajit Narayanan is the inventor of FreeSpeech, a picture language with a deep grammatical structure. He's also the inventor of Avaz, India's first Augmentative and Alternative Communication device for children with disabilities.

"... coverage of problems and techniques are such that more advanced practitioners' might clearly find interest in parts of this book." (Genetic Programming in Evolvable Machinery, Oct 2006)

• Preface ix Acknowledgement xiPART 1 INTRODUCTION 11 Introduction to the Basics of Molecular Biology 31.1 Basic cell architecture 31.2 The structure, content and scale of deoxyribonucleic acid (DNA) 41.3 History of the human genome 91.4 Genes and proteins 101.5 Current knowledge and the ‘central dogma’ 211.6 Why proteins are important 231.7 Gene and cell regulation 241.8 When cell regulation goes wrong 261.9 So, what is bioinformatics? 271.10 Summary of chapter 281.11 Further reading 292 Introduction to Problems and Challenges in Bioinformatics 312.1 Introduction 312.2 Genome 312.3 Transcriptome 402.4 Proteome 502.5 Interference technology, viruses and the immune system 572.6 Summary of chapter 632.7 Further reading 643 Introduction to Artificial Intelligence and Computer Science 653.1 Introduction to search 653.2 Search algorithms 663.3 Heuristic search methods 723.4 Optimal search strategies 763.5 Problems with search techniques 833.6 Complexity of search 843.7 Use of graphs in bioinformatics 863.8 Grammars, languages and automata 903.9 Classes of problems 963.10 Summary of chapter 983.11 Further reading 99PART 2 CURRENT TECHNIQUES 1014 Probabilistic Approaches 1034.1 Introduction to probability 1034.2 Bayes’ Theorem 1054.3 Bayesian networks 1114.4 Markov networks 1164.5 Summary of chapter 1254.6 References 1265 Nearest Neighbour and Clustering Approaches 1275.1 Introduction 1275.2 Nearest neighbour method 1305.3 Nearest neighbour approach for secondary structure protein folding prediction 1325.4 Clustering 1355.5 Advanced clustering techniques 1385.6 Application guidelines 1445.7 Summary of chapter 1455.8 References 1466 Identification (Decision) Trees 1476.1 Method 1476.2 Gain criterion 1526.3 Over fitting and pruning 1576.4 Application guidelines 1606.5 Bioinformatics applications 1636.6 Background 1696.7 Summary of chapter 1706.8 References 1707 Neural Networks 1737.1 Method 1737.2 Application guidelines 1857.3 Bioinformatics applications 1877.4 Background 1927.5 Summary of chapter 1937.6 References 1938 Genetic Algorithms 1958.1 Single-objective genetic algorithms – method 1958.2 Single-objective genetic algorithms – example 2028.3 Multi-objective genetic algorithms – method 2058.4 Application guidelines 2078.5 Genetic algorithms – bioinformatics applications 2108.6 Summary of chapter 2178.7 References and further reading 217PART 3 FUTURE TECHNIQUES 2199 Genetic Programming 2219.1 Method 2219.2 Application guidelines 2309.3 Bioinformatics applications 2329.4 Background 2369.5 Summary of chapter 2369.6 References 23710 Cellular Automata 23910.1 Method 23910.2 Application guidelines 24510.3 Bioinformatics applications 24710.4 Background 25110.5 Summary of chapter 25210.6 References and further reading 25211 Hybrid Methods 25511.1 Method 25511.2 Neural-genetic algorithm for analysing gene expression data 25611.3 Genetic algorithm and k nearest neighbour hybrid for biochemistry solvation 26211.4 Genetic programming neural networks for determining gene – gene interactions in epidemiology 26511.5 Application guidelines 26811.6 Conclusions 26811.7 Summary of chapter 26911.8 References and further reading 269Index 271