Probabilistic Modeling in Bioinformatics and Medical Informatics

Dirk Husmeier, Richard Dybowski, Stephen Roberts (Eds.) - Collection Advanced Information and Knowledge Processing

504 pages, parution le 17/02/2005

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Résumé

Information systems and intelligent knowledge processing are playing an increasing role in business, science and technology. Recently, advanced information systems have evolved to facilitate the co-evolution of human and information networks within communities. These advanced information systems use various paradigms including artificial intelligence, knowledge management, and neural science as well as conventional information processing paradigms.

Probabilistic Modeling in Bioinformatics and Medical Informatics covers recent advances in the use of probabilistic models in computational molecular biology, bioinformatics, and biomedicine.The areas of application covered embrace DMA sequence analysis, phylogenetics, microarray gene expression data analysis, reverse engineering of genetic regulatory networks, pharmacokinetics, biosignal analysis and^medical data integration. Techniques discussed in the book include probabilistic graphical models, neural networks, hidden Markov models, parametric and nonparametric bootstrapping, variational methods, ensemble learning, and Markov chain Monte Carlo simulations. 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.

Probabilistic Modeling in Bioinformatics and Medical Informatics has been written for researchers and students in statistics, informatics, and the biological sciences with Part 1 providing a self-contained introduction to Bayesian networks, neural computation and probabilistic inference, and Parts 2 & 3 demonstrating how these methods are applied in bioinformatics and medical informatics. All three fields are evolving rapidly and this book will be a welcome addition to the field.

Sommaire

Probabilistic Modeling
- A Leisurely Look at Statistical Inference
- Introduction to Learning Bayesian Networks from Data
- A Casual View of Multi-Layer Perceptrons as Probability Models
Bioinformatics
- Introduction 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
Appendix A: Conventions and Notation

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Caractéristiques techniques

	PAPIER
Éditeur(s)	Springer
Auteur(s)	Dirk Husmeier, Richard Dybowski, Stephen Roberts (Eds.)
Collection	Advanced Information and Knowledge Processing
Parution	17/02/2005
Nb. de pages	504
Format	16 x 24
Couverture	Relié
Poids	844g
Intérieur	Noir et Blanc
EAN13	9781852337780
ISBN13	978-1-85233-778-0