Statistical Methods in Bioinformatics: An Introduction

by Warren J. Ewens, Gregory R. Grant

Overview

This book provides an introductory account of probability theory, statistics and shastic process theory appropriate to computational biology and bioinformatics. These topics are important in the analysis of human genome data.

Product Details

ISBN-13:	9780387400822
Publisher:	Springer New York
Publication date:	12/21/2004
Series:	Statistics for Biology and Health
Edition description:	2nd ed. 2005
Pages:	588
Product dimensions:	6.20(w) x 9.40(h) x 1.60(d)

Table of Contents

Probability Theory (i): One Random Variable.- Probability Theory (ii): Many Random Variables.- Statistics (i): An Introduction to Statistical Inference.- Shastic Processes (i): Poisson Processes and Markov Chains.- The Analysis of One DNA Sequence.- The Analysis of Multiple DNA or Protein Sequences.- Shastic Processes (ii): Random Walks.- Statistics (ii): Classical Estimation Theory.- Statistics (iii): Classical Hypothesis Testing Theory.- BLAST.- Shastic Processes (iii): Markov Chains.- Hidden Markov Models.- Gene Expression, Microarrays, and Multiple Testing.- Evolutionary Models.- Phylogenetic Tree Estimation.

Editorial Reviews

From the reviews of the second edition:

"Overall, Ewens and Grant have constructed a needed book in bioinformatics. It should help statisticians understand the emerging field of bioinformatics and serve as an introduction to bioinformatics for a statistician." Journal of the American Statistical Association, March 2006

"This book is the second edition of a book that was based on the content of a two-semester course in bioinformatics and computational biology … . is one of the most important books in this area from the perspective of teaching final year undergraduates and post-graduates in a range of disciplines. … this is a very good book, the best currently available for undergraduates and post-graduates at the intersection of computational biology, bioinformatics, statistics and applied mathematics and a worthwhile improvement on the first edition." (Mark Broom, Journal of the Royal Statistical Society, Vol. 169 (1), 2006)

"This is the second edition of Ewens and Grant’s very well written book on statistical methods in bioinformatics. … The authors have presented an excellent text for a graduate course … . It is clearly and interestingly written and is well organized and has comprehensive references to the literature. The writing style is excellent … . It is … truly a reference book for statistical methods in bioinformatics … . So I strongly recommend the book to both molecular biologists and statisticians … ." (Hamid Pezeshk, ISCB Newsletter, Issue 42, 2006)

"Ewens and Grant aim to fill a gap in the literature on statistics and probability in bioinformatics. … provides a review of the use of familiar statistical techniques and approaches to a new area. … it provides a rigorous treatment of statistical issues associated with bioinformatics tools and a strong statement of the statistical principles and philosophy which needs to underpin these tools. It admirably meets its objectives in thisrespect and is to be recommended." (David Lovell, Pharmaceutical Statistics, Issue 6, 2007)

"The most impressive achievement of this book is its development of blast theory. … The authors pace the knowledge flow smoothly. … The examples and exercises are well thought and highly motivated … . The authors do a fine job of emphasising the false discovery rate … . This book is structured perfectly for a textbook for everyone, statisticians, biologists and computer scientists. … I think this book does an excellent job in introducing many exciting statistical theories." (Lang Li, Briefings in Bioinformatics, Vol. 6 (4), 2005)

"In this book, Ewens and Grant seek to provide a link between bioinformatics and applied statistics. … The book provides detailed discussions of a number of useful distributions and highlights their role in bioinformatics. I found it quite useful and easy to follow. It is a good reference for multidisciplinary research teams in bioinformatics and students on some specialised taught courses." (Kassim S. Mwitondi, Journal of Applied Statistics, Vol. 33 (8), September, 2006)

From the Publisher