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 References appear at the end of each chapter.