Title Probabilistic modeling in bioinformatics and medical informatics / Dirk Husmeier, Richard Dybowski, and Stephen Roberts (eds.)

Published London : Springer, ©2005

Click on the following:
Springer eBooks
Springer Computer Science eBooks

Copies

Description 1 online resource (xix, 504 pages) : illustrations Series Advanced information and knowledge processing, 1610-3947 Advanced information and knowledge processing. 1610-3947 Contents A leisurely look at statistical inference / Dirk Husmeier -- Introduction to learning Bayesian networks from data / Dir Husmeier -- A casual view of multi-layer perceptrons as probability models / Richard Dybowski -- Introduction to statistical phylogenetics / Dirk Husmeier -- Detecting recombination in DNA sequence alignments / Dirk Husmeier, Frank Wright -- RNA-based phylogenetic methods / Magnus Rattray, Paul G. Higgs -- Statistical methods in microarray gene expression data analysis / Claus-Dieter Mayer, Chris A. Glasbey -- Inferring genetic regulatory networks from microarray experiments with Bayesian networks / Dirk Husmeier -- Modeling genetic regulatory networks using gene expression profiling and state-space models / Claudia Rangel [and others] -- An anthology of probabilistic models for medical informatics / Richard Dybowski, Stephen Roberts -- Bayesian analysis of population pharmacokinetic/pharmacodynamic models / David J. Lunn -- Assessing the effectiveness of Bayesian feature selection / Ian T. Nabney [and others] -- Bayes consistent classification of EEG data by approximate marginalization / Peter Sykacek, Iead Rezek, and Stephen Roberts -- Ensemble hidden Markov models with extended observation densities for biosignal analysis / Iead Rezek, Stephen Roberts -- A probabilistic network for fusion of data and knowledge in clinical microbiology / Steen Andreassen [and others] -- Software for probability models in medical informatics / Richard Dybowski Summary 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 Bibliography Includes bibliographical references and index Notes English Print version record In OhioLINK electronic book center SpringerLink Subject Bioinformatics -- Methodology Medical informatics -- Methodology Bayesian statistical decision theory. Computational Biology -- methods Medical Informatics -- methods Bayes Theorem Models, Statistical COMPUTERS -- Bioinformatics. Medical informatics -- Methodology. Bayesian statistical decision theory. Computational Biology -- methods. Medical Informatics -- methods. Bayes Theorem. Models, Statistical. Bioinformatics -- Methodology. Informatique. Bayesian statistical decision theory Bioinformatics -- Methodology Medical informatics -- Methodology Bio-informatica. Geneeskunde. Informatica. Bioinformática. Medicina. Ciência da computação. Genre/Form Electronic books Form Electronic book Author Husmeier, Dirk, 1964- Dybowski, Richard, 1951- Roberts, Stephen, 1965- LC no. 2004051826 ISBN 1852337788 9781852337780 1846281199 9781846281198 6610346658 9786610346653 1280346655 9781280346651

  Permalink