Introduction to Machine Learning and Bioinformatics

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ISBN 9781584886822
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Features

  • Summarizes the latest developments in the fields of bioinformatics and machine learning
  • Provides background on the major problems in bioinformatics
  • Explains the concepts and algorithms of machine learning
  • Uses an abundance of realistic examples to demonstrate the capabilities of key machine learning techniques, such as hidden Markov models and artificial neural networks
  • Applies state-of-the-art machine learning techniques to bioinformatics problems in structural biology, cancer treatment, and proteomics
  • Offers PowerPoint slides and data sets on the authors’ website
  • Summary

    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.

    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.

    Editorial 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