Introduction to Machine Learning and Bioinformatics

Introduction to Machine Learning and Bioinformatics

by Sushmita Mitra, Theodore Perkins, George Michailidis
     
 

ISBN-10: 158488682X

ISBN-13: 9781584886822

Pub. Date: 06/02/2008

Publisher: Taylor & Francis

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

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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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Product Details

ISBN-13:
9781584886822
Publisher:
Taylor & Francis
Publication date:
06/02/2008
Series:
Chapman & Hall/CRC Computer Science & Data Analysis Series
Edition description:
New Edition
Pages:
384
Product dimensions:
6.30(w) x 9.30(h) x 1.10(d)

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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