Probabilistic Modeling in Bioinformatics and Medical Informatics

Dirk Husmeier, Richard Dybowski and Stephen Roberts, editors

ISBN 10: 1852337788 / ISBN 13: 9781852337780

Published by Springer, London, 2005

About this Item

Content Description. A comprehensive interdisciplinary treatment of probabilistic methods in bioinformatics and medical informatics, bringing together statistical modeling, computational biology, machine learning, and biomedical applications. Topics include Bayesian networks, statistical inference, neural networks, phylogenetics, DNA sequence analysis, microarray gene-expression analysis, genetic regulatory networks, pharmacokinetics, biosignal analysis, Hidden Markov models, and probabilistic approaches to clinical and medical data. Written for researchers and advanced students in statistics, informatics, computational biology, bioinformatics, and medical informatics. Part of Springer's Advanced Information and Knowledge Processing series.

Condition Description. Very Good. Unused, apparently unread copy acquired from a bookseller specializing in publisher and distributor overstock, sold to me as New. Interior and page edges are clean, with no writing, highlighting, ownership marks, or other signs of use. Hardcover boards show shelf/storage rubbing and bumped corners, consistent with warehouse storage and handling. Binding remains tight and sound. Conservatively graded Very Good because of the exterior storage wear. Photos are of the actual copy offered for sale.

Seller Inventory # CEBP - 008

Bibliographic Details

• Title: Probabilistic Modeling in Bioinformatics and Medical Informatics
• Publisher: Springer, London
• Publication Date: 2005
• Language: English
• ISBN 10: 1852337788
• ISBN 13: 9781852337780
• Binding: Hardcover
• Condition: Very Good
• Dimensions: 6.1W x 9.25H x 1.1D inches 6.1 inches width by 9.25 inches height by 1.1 inches depth
• Weight: 2 pounds

About this title

Synopsis

Probabilistic Modelling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, machine learning, and the biological sciences. The first part of this book provides a self-contained introduction to the methodology of Bayesian networks. The following parts demonstrate how these methods are applied in bioinformatics and medical informatics. All three fields - the methodology of probabilistic modeling, bioinformatics, and medical informatics - are evolving very quickly. The text should therefore be seen as an introduction, offering both elementary tutorials as well as more advanced applications and case studies.

Review

From the reviews:

"This book is a collection of chapters describing methods of statistical analysis of medical and biological data, with a focus on mathematical descriptions and implementing algorithms. ... It will be particularly useful for those who are interested in a better understanding of artificial neutral networks ... . Generally, it is a refreshing book for a statistician ... giving a good description of a wide variety of complex models." (Natalia Bochkina, Significance, Vol. 3 (3), 2006)

"This book covers recent advances in the use of probabilistic models in computational molecular biology, bioinformatics and biomedicine. ... A self-contained chapter on statistical inference is included as well as a discussion of Bayesian networks as a common and unifying framework for probabilistic modeling. The book has been written for researchers and students in statistics, informatics, and biological sciences ... . Finally, an appendix explains the conventions and notation used throughout the book." (T. Postelnicu, Zentralblatt MATH, Vol. 1151, 2009)