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#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'.
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#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; 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
... 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)
