Probabilistic Modeling in Bioinformatics and Medical Informatics

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. TOC:From the Contents. A Leisurely Look at Statistical Inference. Introduction to Learning Bayesian Networks from Data. A Casual View of Multi-Layer Perceptrons as Probability Models.- 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. Modelling Genetic Regulatory Networks using Gene Expression Profiling and State Space Models.- 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 Marginalisation. 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.