Introduction to Machine Learning and Bioinformatics

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Summarizes the latest developments in the fields of bioinformatics and machine learning Provides background on the major problems in bioinformatics Explains the concepts and algorithms of machine learning Uses an abundance of realistic examples to demonstrate the capabilities of key machine learning techniques, such as hidden Markov models and artificial neural networks Applies state-of-the-art machine learning techniques to bioinformatics problems in structural biology, cancer treatment, and proteomics Offers PowerPoint slides and data sets on the authors’ website Lucidly Integrates Current Activities Focusing on both fundamentals and recent advances, Introduction to Machine Learning and Bioinformatics presents an informative and accessible account of the ways in which these two increasingly intertwined areas relate to each other. Examines Connections between Machine Learning & Bioinformatics The book begins with a brief historical overview of the technological developments in biology. It then describes the main problems in bioinformatics and the fundamental concepts and algorithms of machine learning. After forming this foundation, the authors explore how machine learning techniques apply to bioinformatics problems, such as electron density map interpretation, biclustering, DNA sequence analysis, and tumor classification. They also include exercises at the end of some chapters and offer supplementary materials on their website. Explores How Machine Learning Techniques Can Help Solve Bioinformatics Problems Shedding light on aspects of both machine learning and bioinformatics, this text shows how the innovative tools and techniques of machine learning help extract knowledge from the deluge of information produced by today’s biological experiments. Subjects of the book : Life Sciences Biotechnology Contents of the book : Introduction The Biology of a Living Organism Cells DNA and Genes Proteins Metabolism Biological Regulation Systems: When They Go Awry Measurement Technologies Probabilistic and Model-Based Learning Introduction: Probabilistic Learning Basics of Probability Random Variables and Probability Distributions Basics of Information Theory Basics of Stochastic Processes Hidden Markov Models Frequentist Statistical Inference Some Computational Issues Bayesian Inference Exercises Classification Techniques Introduction and Problem Formulation The Framework Classification Methods Applications of Classification Techniques to Bioinformatics Problems Exercises Unsupervised Learning Techniques Introduction Principal Components Analysis Multidimensional Scaling Other Dimension Reduction Techniques Cluster Analysis Techniques Exercises Computational Intelligence in Bioinformatics Introduction Fuzzy Sets Artificial Neural Networks Evolutionary Computing Rough Sets Hybridization Application to Bioinformatics Conclusion Exercises Connections Sequence Analysis Analysis of High-Throughput Gene Expression Data Network Inference Exercises Machine Learning in Structural Biology Introduction Background arp/warp resolve textal acmi Conclusion Soft Computing in Biclustering Introduction Biclustering Multiobjective Biclustering Fuzzy Possibilistic Biclustering Experimental Results Conclusions and Discussion Bayesian Methods for Tumor Classification Introduction Classification Based on Reproducing Kernel Hilbert Spaces Hierarchical Classification Model Likelihoods of RKHS Models The Bayesian Analysis Prediction and Model Choice Some Examples Concluding Remarks Modeling and Analysis of iTRAQ Data Introduction Statistical Modeling of iTRAQ Data Data Illustration Discussion and Concluding Remarks Mass Spectrometry Classification Introduction Background on Proteomics Classification Methods Data and Implementation Results and Discussion Conclusions Acknowledgment Index