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 consists of 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 course...
and] is equally well suited to individual study.... 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 Statistical Association)
"The authors have restructured classical material to a great
extent and the new organization of the
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