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Binding: PaperbackDewey Decimal Number: 572.8633 EAN: 9780521629713 ISBN: 0521629713 Label: Cambridge University Press Manufacturer: Cambridge University Press Number Of Items: 1 Number Of Pages: 356 Publication Date: July 01, 1999 Publisher: Cambridge University Press Studio: Cambridge University Press Editorial Review: Product Description: Probablistic models are becoming increasingly important in analyzing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analyzing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it is accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time presents the state of the art in this new and important field. Book Description: Probabilistic methods are assuming greater significance in the analysis of nucleotide sequence data. This book provides the first unified, up-to-date and self-contained account of such methods, and more generally of probabilistic methods of sequence analysis, presented in a Bayesian framework. Average Rating:
 Rating:  - Technically brilliant but totally inaccessibleWhile this is perhaps the best book on Hidden Markov Models in Bioinformatics available, you would do well to read Rabiner's review paper. For me this is the type of book that would put potential students off bioinformatics for life. It is too technical and uses inappropriate notation. It has too many "It is easily shown" phrases which means that actually the real proof would be rather involved. Dynamic programming is not explained very well. If you have a maths or computer background ... Read More Rating:  - An Excellent IntroductionThis book gives an excellent introduction into sequence analysis for a person who is already somewhat familiar with the basics of Bayesian techniques. The authors illustrate concepts, as and when they are introduced, via carefully selected examples; comprehension is made much easier because of this. Rating:  - Great referenceA great reference and a good introduction to many important concepts in sequence analysis. However, if you don't have a reasonable grounding in math you may struggle with the terse notation. Borodovsky's companion book is an excellent partner for this book. Get both. Rating: - One of the best availableAlthough this book is based primarily on work that was completed in 1998, and therefore somewhat out of date, it is the best book I have found for teaching bioinformatics. I selected this as the best of the available books on the subject for use in my bioinformatics and numerical methods course which is to be taught in the fall of 2007 at Univ. of Conn. This course is an upper division undergraduate and first year graduate course. That is roughly the level of this text and the comparative advantage ... Read More Rating: - Truly an Excellent BookI will agree and submit: this is an invaluable introduction to the field of bioinformatics. With introductions to everything from sequence analysis to hidden markov models and even a primer on grammars, this is a useful introduction both to biological applications for computer scientists *as well as* computational methods for biologists. I am in a joint graduate-level biology/computer science class and we are using this book as a foundation to bring both groups up to speed and it seems to ... Read More |
