Introduction to Machine Learning and Bioinformatics: 1st Edition (Hardback) book cover

Introduction to Machine Learning and Bioinformatics

1st Edition

By Sushmita Mitra, Sujay Datta, Theodore Perkins, George Michailidis

Chapman and Hall/CRC

384 pages | 62 B/W Illus.

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pub: 2008-06-05
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Description

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.

Reviews

… 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

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.

About the Series

Chapman & Hall/CRC Computer Science & Data Analysis

Learn more…

Subject Categories

BISAC Subject Codes/Headings:
BUS061000
BUSINESS & ECONOMICS / Statistics
COM037000
COMPUTERS / Machine Theory
MAT029000
MATHEMATICS / Probability & Statistics / General