Introduction to Machine Learning and Bioinformatics

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Overview

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.

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Editorial Reviews

From the Publisher
… The stated audience for this book is M.S. and Ph.D. students in bioinformatics, machine intelligence, applied statistics, biostatistics, computer science, and related areas. … a well-written collection from multiple authors that I recommend for the intended audience. Several chapters include exercises.
Technometrics, November 2009, Vol. 51, No. 4

…a good text/reference book that summarizes the latest developments in the interface between bioinformatics and machine learning and offer[s] a thorough introduction to each field. … One of the strengths of this book is the clear notation with a mathematical and statistical flavor, which will be attractive to Biometrics readers, especially to those new to statistical learning and data mining. It is also very readable for a variety of interested learners, researchers, and audiences from various backgrounds and disciplines. …
Biometrics, March 2009

… a well-structured book that is a good starting point for machine learning in bioinformatics. … Using many popular examples, the statistical theory becomes comprehensible and bioinformatics examples motivate [readers] to apply the concepts to real data.
—Markus Schmidberger, Journal of Statistical Software, November 2008

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Product Details

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.

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