Probabilistic Modeling in Bioinformatics and Medical Informatics

1. Auflage. 2005
ISBN: 978-1-85233-778-0
Verlag: Springer

Buch, Englisch, 508 Seiten, HC runder Rücken kaschiert, Format (B × H): 160 mm x 241 mm, Gewicht: 2010 g

Reihe: Advanced Information and Knowledge Processing

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.

Zielgruppe

Post-graduate students, researchers, professionals in fields of medicine; bioinformatics; statistics; machine learning

Autoren/Hrsg.

Husmeier, Dirk

Roberts, Stephen

Dybowski, Richard

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Naturwissenschaften Biowissenschaften Angewandte Biologie Bioinformatik
• Mathematik | Informatik EDV | Informatik Informatik Logik, formale Sprachen, Automaten
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Computeranwendungen in der Mathematik
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Bioinformatik
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizinische Mathematik & Informatik

Inhaltsverzeichnis

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.