Table of Contents

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.