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Probabilistic Modeling in Bioinformatics and Medical Informatics

  • Book
  • © 2005

Overview

Editors:
  1. Dirk Husmeier

    1. Biomathematics and Statistics-BioSS, UK

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    You can also search for this editor in PubMed   Google Scholar

  2. Richard Dybowski

    1. InferSpace, UK

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    You can also search for this editor in PubMed   Google Scholar

  3. Stephen Roberts

    1. Oxford University, UK

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    You can also search for this editor in PubMed   Google Scholar

  • There is currently no other book that provides a contemporary survey of probabilistic models in both bio-medicine and bio-informatics
  • An internet site will accompany the book
  • Includes supplementary material: sn.pub/extras

Part of the book series: Advanced Information and Knowledge Processing (AI&KP)

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Table of contents (16 chapters)

  1. Front Matter

    Pages i-xix
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  2. Probabilistic Modeling

    1. A Leisurely Look at Statistical Inference

      • Dirk Husmeier
      Pages 3-15

    2. Introduction to Learning Bayesian Networks from Data

      • Dirk Husmeier
      Pages 17-57

    3. A Casual View of Multi-Layer Perceptrons as Probability Models

      • Richard Dybowski
      Pages 59-80

  3. Bioinformatics

    1. Introduction to Statistical Phylogenetics

      • Dirk Husmeier
      Pages 83-145

    2. Detecting Recombination in DNA Sequence Alignments

      • Dirk Husmeier, Frank Wright
      Pages 147-190

    3. RNA-Based Phylogenetic Methods

      • Magnus Rattray, Paul G. Higgs
      Pages 191-210

    4. Statistical Methods in Microarray Gene Expression Data Analysis

      • Claus-Dieter Mayer, Chris A. Glasbey
      Pages 211-238

    5. Inferring Genetic Regulatory Networks from Microarray Experiments with Bayesian Networks

      • Dirk Husmeier
      Pages 239-267

    6. Modeling Genetic Regulatory Networks using Gene Expression Profiling and State-Space Models

      • Claudia Rangel, John Angus, Zoubin Ghahramani, David L. Wild
      Pages 269-293

  4. Medical Informatics

    1. An Anthology of Probabilistic Models for Medical Informatics

      • Richard Dybowski, Stephen Roberts
      Pages 297-349

    2. Bayesian Analysis of Population Pharmacokinetic/Pharmacodynamic Models

      • David J. Lunn
      Pages 351-370

    3. Assessing the Effectiveness of Bayesian Feature Selection

      • Ian T. Nabney, David J. Evans, Yann Brulé, Caroline Gordon
      Pages 371-389

    4. Bayes Consistent Classification of EEG Data by Approximate Marginalization

      • Peter Sykacek, Iead Rezek, Stephen Roberts
      Pages 391-417

    5. Ensemble Hidden Markov Models with Extended Observation Densities for Biosignal Analysis

      • Iead Rezek, Stephen Roberts
      Pages 419-450

    6. A Probabilistic Network for Fusion of Data and Knowledge in Clinical Microbiology

      • Steen Andreassen, Leonard Leibovici, Mical Paul, Anders D. Nielsen, Alina Zalounina, Leif E. Kristensen et al.
      Pages 451-472

    7. Software for Probability Models in Medical Informatics

      • Richard Dybowski
      Pages 473-489

  5. Back Matter

    Pages 491-504
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Keywords

About this book

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.

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)

Editors and Affiliations

  • Biomathematics and Statistics-BioSS, UK

    Dirk Husmeier

  • InferSpace, UK

    Richard Dybowski

  • Oxford University, UK

    Stephen Roberts

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