Overview

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.

Full Product Details

Author:   Dirk Husmeier ,  Richard Dybowski ,  Stephen Roberts
Publisher:   Springer London Ltd
Imprint:   Springer London Ltd
Edition:   Softcover reprint of hardcover 1st ed. 2005
Dimensions:   Width: 15.50cm , Height: 2.60cm , Length: 23.50cm
Weight:   0.795kg
ISBN:  

9781849969123

ISBN 10:   1849969124
Pages:   508
Publication Date:   22 October 2010
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Probabilistic Modeling.- A Leisurely Look at Statistical Inference.- to Learning Bayesian Networks from Data.- A Casual View of Multi-Layer Perceptrons as Probability Models.- Bioinformatics.- to Statistical Phylogenetics.- Detecting Recombination in DNA Sequence Alignments.- RNA-Based Phylogenetic Methods.- Statistical Methods in Microarray Gene Expression Data Analysis.- Inferring Genetic Regulatory Networks from Microarray Experiments with Bayesian Networks.- Modeling Genetic Regulatory Networks using Gene Expression Profiling and State-Space Models.- Medical Informatics.- An Anthology of Probabilistic Models for Medical Informatics.- Bayesian Analysis of Population Pharmacokinetic/Pharmacodynamic Models.- Assessing the Effectiveness of Bayesian Feature Selection.- Bayes Consistent Classification of EEG Data by Approximate Marginalization.- Ensemble Hidden Markov Models with Extended Observation Densities for Biosignal Analysis.- A Probabilistic Network for Fusion of Data and Knowledge in Clinical Microbiology.- Software for Probability Models in Medical Informatics.

Reviews

"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)"

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)

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