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.
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.
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