Introduction to Machine Learning and Bioinformatics 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
References appear at the end of each chapter.