Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems
By: Ajit Narayanan (author), Edward Keedwell (author)Hardback
1 - 2 weeks availability
Bioinformatics is contributing to some of the most important advances in medicine and biology. At the forefront of this exciting new subject are techniques known as artificial intelligence which are inspired by the way in which nature solves the problems it faces. This book provides a unique insight into the complex problems of bioinformatics and the innovative solutions which make up 'intelligent bioinformatics'. Intelligent Bioinformatics requires only rudimentary knowledge of biology, bioinformatics or computer science and is aimed at interested readers regardless of discipline. Three introductory chapters on biology, bioinformatics and the complexities of search and optimisation equip the reader with the necessary knowledge to proceed through the remaining eight chapters, each of which is dedicated to an intelligent technique in bioinformatics. The book also contains many links to software and information available on the internet, in academic journals and beyond, making it an indispensable reference for the 'intelligent bioinformatician'.
Intelligent Bioinformatics will appeal to all postgraduate students and researchers in bioinformatics and genomics as well as to computer scientists interested in these disciplines, and all natural scientists with large data sets to analyse.
Preface. Acknowledgements. PART 1: INTRODUCTION. 1. Introduction to the Basics of Molecular Biology. 1.1 Basic cell architecture. 1.2 The structure, content and scale of deoxyribonucleic acid (DNA). 1.3 History of the human genome. 1.4 Genes and proteins. 1.5 Current knowledge and the 'central dogma'. 1.6 Why proteins are important. 1.7 Gene and cell regulation. 1.8 When cell regulation goes wrong. 1.9 So, what is bioinformatics? 1.10 Summary of chapter. 1.11 Further reading. 2. Introduction to Problems and Challenges in Bioinformatics. 2.1 Introduction. 2.2 Genome. 2.3 Transcriptome. 2.4 Proteome. 2.5 Interference technology, viruses and the immune system. 2.6 Summary of chapter. 2.7 Further reading. 3. Introduction to Artificial Intelligence and Computer Science. 3.1 Introduction to search. 3.2 Search algorithms. 3.3 Heuristic search methods. 3.4 Optimal search strategies. 3.5 Problems with search techniques. 3.6 Complexity of search. 3.7 Use of graphs in bioinformatics. 3.8 Grammars, languages and automata. 3.9 Classes of problems. 3.10 Summary of chapter. 3.11 Further reading. PART 2: CURRENT TECHNIQUES. 4. Probabilistic Approaches. 4.1 Introduction to probability. 4.2 Bayes' Theorem. 4.3 Bayesian networks. 4.4 Markov networks. 4.5 References. 5. Nearest Neighbour and Clustering Approaches. 5.1 Introduction. 5.2 Nearest neighbour method. 5.3 Nearest neighbour approach for secondary structure protein folding prediction. 5.4 Clustering. 5.5 Advanced clustering techniques. 5.6 Application guidelines. 5.7 Summary of chapter. 5.8 References. 6. Identification (Decision) Trees. 6.1 Method. 6.2 Gain criterion. 6.3 Over fitting and pruning. 6.4 Application guidelines. 6.5 Bioinformatics applications. 6.6 Background. 6.7 Summary of chapter. 6.8 References. 7. Neural Networks. 7.1 Method. 7.2 Application guidelines. 7.3 Bioinformatics applications. 7.4 Background. 7.5 Summary of chapter. 7.6 References. 8. Genetic Algorithms. 8.1 Single-objective genetic algorithms - method. 8.2 Single-objective genetic algorithms - example. 8.3 Multi-objective genetic algorithms - method. 8.4 Application guidelines. 8.5 Genetic algorithms - bioinformatics applications. 8.6 Summary of chapter. 8.7 References and Further Reading. PART 3: FUTURE TECHNIQUES 9. Genetic Programming. 9.1 Method. 9.2 Application guidelines. 9.3 Bioinformatics applications. 9.4 Background. 9.5 Summary of chapter. 9.6 References. 10. Cellular Automata. 10.1 Method. 10.2 Application guidelines. 10.3 Bioinformatics applications. 10.4 Background. 10.5 Summary of chapter. 10.6 References and Further Reading. 11. Hybrid Methods. 11.1 Method. 11.2 Neural-genetic algorithm for analyzing gene expression data. 11.3 Genetic algorithm and k nearest neighbour hybrid for biochemistry solvation. 11.4 Genetic programming neural networks for determining gene-gene interactions in epidemiology. 11.5 Application guidelines. 11.6 Conclusions. 11.7 Summary of chapter. References and Further Reading. Index.
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