Markus Schmidberger

English

Research Papers in Economics

JSS Journal of Statistical Software
November 2008, Volume 28, Book Review 2. http://www.jstatsoft.org/
Reviewer: Markus Schmidberger
Ludwig-Maximilians-Universit at M unchen
Introduction to Machine Learning and Bioinformatics
Sushmita Mitra, Sujay Datta, Theodore Perkins, and George Michailidis
Chapman & Hall/CRC, Boca Raton, Florida, 2008.
ISBN 978-1-58488-682-2. 384 pp. USD 79.95.
Introduction
Machine learning (Hastie et al. 2001) is a sub-set of articial intelligence and deals with
techniques to allow computers to learn. Bioinformatics is the application of information
technology to the area of molecular biology. For both disciplines there are already many
books introducing the basic concepts and there are some books presenting machine learning
applications in the area of bioinformatics. The book under review covers essentially topics of
machine learning and bioinformatics and demonstrates the usefulness of statistical methods in
well-documented bioinformatic examples. In the rst part, the book teaches basic concepts of
machine learning and introduces essential biological aspects. In the second part, the authors
close the gap between the two disciplines and present a lot of interesting examples. It is a
well-structured book that is a good starting point for machine learning in bioinformatics.
The book has 12 chapters which can be grouped in two parts: Chapters 1{6 cover the intro-
duction and the basics. Chapter 7 describes the connection between machine learning and
bioinformatics. Chapters 8{12 include well-selected and ongoing bioinformatic examples and
are primarily written by co-authors.
The authors put a lot of their teaching experiences into this book. The statistical basics are
illustrated with well-chosen and popular examples. Every chapter (except Chapter 2) ends
with exercises and references.
Book contents
Chapter 1 starts with the historical development of the amount of biological data and intro-
duces connections and dependencies between statistics, biology, and computer science.
Chapter 2 covers a brief review of the essential concepts in cellular and molecular biology
to motivate the biological problems. It starts with the basic unit of living organisms (cells),2 Introduction to Machine Learning and Bioinformatics
DNA and biological regulation systems and ends with measurement technologies for molecular
biology. This chapter requires no biological knowledge but is important for the following
chapters. Biologists can skip this chapter.
Chapter 3 introduces the statistical basics: probability theory, stochastic processes, hidden
Markov models, etc. This chapter requires no statistical knowledge but is important for the
following chapters. Statisticians can skip this chapter.
Classication, also known as supervised learning, is required in a lot of dierent disciplines.
Through the power of computers new classiers were developed in the last years: classication
trees, support vector machines, boosting, etc. Chapter 4 describes the general statistical
decision framework for classication and discusses a number of classication methods.
In classication it is often reasonable to use dimension reduction methods to simplify the
data. Chapter 5 covers components analysis and multidimensional scaling for dimension
reduction. Furthermore, a problem formulation and established algorithms for cluster analysis
are described. Cluster analysis is the classication of a set of objects in dierent groups and
a form of unsupervised learning.
In Chapter 6 the authors introduce computational intelligence in a very brief way. The
chapter ends with a long discussion about opportunities and applications for computational
intelligence in dierent areas of bioinformatics.
In Chapter 7 the authors describe connections between bioinformatics and machine learn-
ing. For this they select three well-known problem areas: sequence analysis, gene expression
analysis, and inference of expression data.
Machine learning and computer vision algorithms can be used for automated electron-density
map interpretation. Chapter 8 gives a short introduction into structural analysis and the
automatic interpretation of 3D protein images. This chapter is more algorithmically based
and describes four interpretation methods in detail.
Based on the high-dimensionality of gene expression data Chapter 9 deals with biclustering
as a data mining strategy. Biclustering allows for clustering of the rows (genes) and columns
(samples), simultaneously. The state-of-the-art algorithms are presented, benchmarked and
discussed for the gene expression Yeast data.
Chapter 10 focuses on the problem of classication based on microarray data. They de-
scribe an approach using all genes|rather than eliminating more than 90%|for classication:
Bayesian binary classication models for prediction based on a reproducing kernel Hilbert
space. The chapter concludes with a benchmark over three publicly available datasets.
iTRAQ is a technique used to identify and quantify proteins from dierent sources in one
single experiment. Chapter 11 very briey presents a statistical model to describe sources of
variation in the data. The model allows for testing of the hypothesis of dierential expression
of proteins.
The book ends with Chapter 12 with a review of automated techniques for classication of
database search results and the application to proteomics.
Conclusion
Actually, there is one other book which has a very similar content and structure: Baldi and
Brunak (2001). Based on the clear structure, the basic chapters and the understandableJournal of Statistical Software { Book Reviews 3
examples the book under review seems to be suitable as a textbook for a graduate course.
Up to the time of review, there were no solutions for the examples available and no further
information on the website.
The authors concentrated on statistical aspects and motivating examples, but they neglected
the computational aspects. This means there are only a few printed code examples or details
concerning software and implementation aspects. Most of the described machine learning
techniques are already implemented for example in packages for the R system (R Development
Core Team 2008) and adapted bioinformatic applications are available in the Bioconductor
project (Gentleman et al. 2004).
The authors selected an interesting and very comprehensive way to present mathematical
aspects without using the theorem-proof syntax. Especially the very well-known examples
help to understand the theory.
In summary, in the book under review the authors introduce the reader to machine learning
and bioinformatics. Using many popular examples, the statistical theory becomes compre-
hensible and bioinformatic examples motivate to apply the concepts to real data.
References
Baldi P, Brunak S (2001). Bioinformatics: The Machine Learning Approach. MIT Press,
Cambridge. ISBN 0-262-02506-X.
Gentleman RC, Carey VJ, Bates DM, Bolstad B, Dettling M, Dudoit S, Ellis B, Gautier L,
Ge Y, Gentry J, Hornik K, Hothorn T, Huber W, Iacus S, Irizarry R, Li FLC, Maechler M,
Rossini AJ, Sawitzki G, Smith C, Smyth G, Tierney L, Yang JYH, Zhang J (2004). \Bio-
conductor: Open Software Development for Computational Biology and Bioinformatics."
Genome Biology, 5, R80. URL http://genomebiology.com/2004/5/10/R80.
Hastie T, Tibshirani R, Friedman JH (2001). The Elements of Statistical Learning. Springer-
Verlag, New York. ISBN 0-387-95284-5.
R Development Core Team (2008). R: A Language and Environment for Statistical Computing.
R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http:
//www.R-project.org/.
Reviewer:
Markus Schmidberger
Chair of Biometrics and Bioinformatics
Ludwig-Maximilians-Universit at M unchen
Marchioninistr. 15
D-81377 M unchen, Germany4 Introduction to Machine Learning and Bioinformatics
Currently at:
Computational Biology Program
Fred Hutchinson Cancer Research Center
Seattle, WA, United States of America
E-mail: Markus.Schmidberger@ibe.med.uni-muenchen.de
Journal of Statistical Software http://www.jstatsoft.org/
published by the American Statistical Association http://www.amstat.org/
Volume 28, Book Review 2 Published: 2008-11-04
November 2008

Bioconductor: Open Software Development for Computational Biology and Bioinformatics.&quot;

Introduction to Machine Learning and Bioinformatics

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