Edward Keedwell, Ajit Narayanan

Intelligent Bioinformatics (eBook, PDF)

The Application of Artificial Intelligence Techniques to Bioinformatics Problems

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Seitenzahl: 294
• Erscheinungstermin: 13. Dezember 2005
• Englisch
• ISBN-13: 9780470021767
• Artikelnr.: 37289696

Autorenporträt

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.

Inhaltsangabe

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.

Rezensionen

"... 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)