Intelligent Bioinformatics: The Application of Artificial Intelligence Techniques to Bioinformatics Problems

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.

[1]  Belur V. Dasarathy,et al.  Nearest neighbor (NN) norms: NN pattern classification techniques , 1991 .

[2]  John R. Koza,et al.  Genetic generation of both the weights and architecture for a neural network , 1991, IJCNN-91-Seattle International Joint Conference on Neural Networks.

[3]  S. Kauffman.  At Home in the Universe: The Search for the Laws of Self-Organization and Complexity , 1995 .

[4]  Gary D. Stormo,et al.  Modeling Regulatory Networks with Weight Matrices , 1998, Pacific Symposium on Biocomputing.

[5]  Ajit Narayanan,et al.  Mining viral protease data to extract cleavage knowledge , 2002, ISMB.

[6]  K. Chou,et al.  Protein subcellular location prediction. , 1999, Protein engineering.

[7]  Ed Keedwell,et al.  Single-layer artificial neural networks for gene expression analysis , 2004, Neurocomputing.

[8]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[9]  Hitoshi Iba,et al.  Inference of gene regulatory model by genetic algorithms , 2001, Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546).

[10]  Araceli M. Huerta,et al.  From specific gene regulation to genomic networks: a global analysis of transcriptional regulation in Escherichia coli. , 1998, BioEssays : news and reviews in molecular, cellular and developmental biology.

[11]  F ROSENBLATT,et al.  The perceptron: a probabilistic model for information storage and organization in the brain. , 1958, Psychological review.

[12]  David Corne,et al.  Evolutionary Computation In Bioinformatics , 2003 .

[13]  C Sander,et al.  Predicting protein structure using hidden Markov models , 1997, Proteins.

[14]  Michael Ruogu Zhang,et al.  Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. , 1998, Molecular biology of the cell.

[15]  Heinz-Theodor Mevissen,et al.  Decision tree-based formation of consensus protein secondary structure prediction , 1999, Bioinform..

[16]  C K Cheng,et al.  A cellular automata model of enzyme kinetics. , 1996, Journal of molecular graphics.

[17]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[18]  Michael L. Raymer,et al.  GA-Facilitated Knowledge Discovery and Pattern Recognition Optimization Applied to the Biochemistry of Protein Solvation , 2004, GECCO.

[19]  Jason H. Moore,et al.  Genetic Programming Neural Networks as a Bioinformatics Tool for Human Genetics , 2004, GECCO.

[20]  Roger E Bumgarner,et al.  Clustering gene-expression data with repeated measurements , 2003, Genome Biology.

[21]  D. Rumelhart.  Parallel Distributed Processing Volume 1: Foundations , 1987 .

[22]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[23]  Ed Keedwell,et al.  Genetic Algorithms for Gene Expression Analysis , 2003, EvoWorkshops.

[24]  Huiqing Liu,et al.  Discovery of significant rules for classifying cancer diagnosis data , 2003, ECCB.

[25]  Arno Lukas,et al.  Discrete simulation of regulatory homo- and heterodimerization in the apoptosis effector phase , 2002, Bioinform..

[26]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[27]  D. Botstein,et al.  Cluster analysis and display of genome-wide expression patterns. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[28]  Peter J. Fleming,et al.  An Overview of Evolutionary Algorithms in Multiobjective Optimization , 1995, Evolutionary Computation.

[29]  M. Ringnér,et al.  Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks , 2001, Nature Medicine.

[30]  Edward Keedwell,et al.  Novel cellular automata approach to optimal water distribution network design , 2006 .

[31]  D E Heckerman,et al.  An evaluation of the diagnostic accuracy of Pathfinder. , 1992, Computers and biomedical research, an international journal.

[32]  Barry Robson,et al.  An algorithm for secondary structure determination in proteins based on sequence similarity , 1986, FEBS letters.

[33]  Bill C White,et al.  Optimization of neural network architecture using genetic programming improves detection and modeling of gene-gene interactions in studies of human diseases , 2003, BMC Bioinformatics.

[34]  K C Chou,et al.  Artificial neural network model for predicting HIV protease cleavage sites in protein , 1998 .

[35]  R. Sokal,et al.  A QUANTITATIVE APPROACH TO A PROBLEM IN CLASSIFICATION† , 1957, Evolution; International Journal of Organic Evolution.

[36]  Simon Kasif,et al.  Protein Secondary-Structure Modeling with Probabilistic Networks , 1993, ISMB.

[37]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[38]  Kuo-Chen Chou,et al.  Artificial Neural Network Model for Predicting Protein Subcellular Location , 2002, Comput. Chem..

[39]  Jon Louis Bentley,et al.  An Algorithm for Finding Best Matches in Logarithmic Expected Time , 1977, TOMS.

[40]  G J Barton,et al.  Evaluation and improvement of multiple sequence methods for protein secondary structure prediction , 1999, Proteins.

[41]  J. M. Levin,et al.  Exploring the limits of nearest neighbour secondary structure prediction. , 1997, Protein engineering.

[42]  Tony Curzon Price,et al.  Emergence: From Chaos to Order by John H. Holland , 1998, J. Artif. Soc. Soc. Simul..

[43]  Marvin Minsky,et al.  Perceptrons: An Introduction to Computational Geometry , 1969 .

[44]  D. Higgins,et al.  SAGA: sequence alignment by genetic algorithm. , 1996, Nucleic acids research.

[45]  John R. Koza,et al.  Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.

[46]  Teuvo Kohonen,et al.  The self-organizing map , 1990 .

[47]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[48]  Anders Krogh,et al.  SAM: SEQUENCE ALIGNMENT AND MODELING SOFTWARE SYSTEM , 1995 .