Detailansicht
Intelligent Bioinformatics
The Application of Artificial Intelligence Techniques to Bioinformatics Problems
ISBN/EAN: 9780470021750
Umbreit-Nr.: 1773005
Sprache:
Englisch
Umfang: 294 S.
Format in cm:
Einband:
gebundenes Buch
Erschienen am 22.04.2005
Auflage: 1/2005
- Zusatztext
- InhaltsangabePreface. 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 Singleobjective genetic algorithms method. 8.2 Singleobjective genetic algorithms example. 8.3 Multiobjective 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 Neuralgenetic 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.