Advances in computers and biotechnology have had a profound impact
on biomedical research, and as a result complex data sets can now
be generated to address extremely complex biological questions.
Correspondingly, advances in the statistical methods necessary to
analyze such data are following closely behind the advances in data
generation methods. The statistical methods required by
bioinformatics present many new and difficult problems for the
research community.
This book provides an introduction to some of these new methods.
The main biological topics treated include sequence analysis,
BLAST, microarray analysis, gene finding, and the analysis of
evolutionary processes. The main statistical techniques covered
include hypothesis testing and estimation, Poisson processes,
Markov models and Hidden Markov models, and multiple testing
methods.
The second edition features new chapters on microarray analysis
and on statistical inference, including a discussion of ANOVA, and
discussions of the statistical theory of motifs and methods based
on the hypergeometric distribution. Much material has been
clarified and reorganized.
The book is written so as to appeal to biologists and computer
scientists who wish to know more about the statistical methods of
the field, as well as to trained statisticians who wish to become
involved with bioinformatics. The earlier chapters introduce the
concepts of probability and statistics at an elementary level, but
with an emphasis on material relevant to later chapters and often
not covered in standard introductory texts. Later chapters should
be immediately accessible to the trained statistician. Sufficient
mathematical background consistsof introductory courses in calculus
and linear algebra. The basic biological concepts that are used are
explained, or can be understood from the context, and standard
mathematical concepts are summarized in an Appendix. Problems are
provided at the end of each chapter allowing the reader to develop
aspects of the theory outlined in the main text.
Warren J. Ewens holds the Christopher H. Brown Distinguished
Professorship at the University of Pennsylvania. He is the author
of two books, Population Genetics and Mathematical Population
Genetics. He is a senior editor of Annals of Human Genetics and has
served on the editorial boards of Theoretical Population Biology,
GENETICS, Proceedings of the Royal Society B and SIAM Journal in
Mathematical Biology. He is a fellow of the Royal Society and the
Australian Academy of Science.
Gregory R. Grant is a senior bioinformatics researcher in the
University of Pennsylvania Computational Biology and Informatics
Laboratory. He obtained his Ph.D. in number theory from the
University of Maryland in 1995 and his Masters in Computer Science
from the University of Pennsylvania in 1999.
Comments on the First Edition. "This book would be an ideal text
for a postgraduate coursea ][and] is equally well suited to
individual studya ]. I would recommend the book highly"
(Biometrics). "Ewens and Grant have given us a very welcome
introduction to what is behind those pretty [graphical user]
interfaces" (Naturwissenschaften.). "The authors do an excellent
job of presenting the essence of the material without getting
bogged down in mathematical details" (Journal. American Staistical.
Association). "The authors have restructured classical materialto a
great extent and the new organization of the different topics is
one of the outstanding services of the book" (Metrika).
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