Ebook: 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.

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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.

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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.
Content:
Front Matter....Pages i-xix
A Leisurely Look at Statistical Inference....Pages 3-15
Introduction to Learning Bayesian Networks from Data....Pages 17-57
A Casual View of Multi-Layer Perceptrons as Probability Models....Pages 59-80
Introduction to Statistical Phylogenetics....Pages 83-145
Detecting Recombination in DNA Sequence Alignments....Pages 147-190
RNA-Based Phylogenetic Methods....Pages 191-210
Statistical Methods in Microarray Gene Expression Data Analysis....Pages 211-238
Inferring Genetic Regulatory Networks from Microarray Experiments with Bayesian Networks....Pages 239-267
Modeling Genetic Regulatory Networks using Gene Expression Profiling and State-Space Models....Pages 269-293
An Anthology of Probabilistic Models for Medical Informatics....Pages 297-349
Bayesian Analysis of Population Pharmacokinetic/Pharmacodynamic Models....Pages 351-370
Assessing the Effectiveness of Bayesian Feature Selection....Pages 371-389
Bayes Consistent Classification of EEG Data by Approximate Marginalization....Pages 391-417
Ensemble Hidden Markov Models with Extended Observation Densities for Biosignal Analysis....Pages 419-450
A Probabilistic Network for Fusion of Data and Knowledge in Clinical Microbiology....Pages 451-472
Software for Probability Models in Medical Informatics....Pages 473-489
Back Matter....Pages 491-504

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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.
Content:
Front Matter....Pages i-xix
A Leisurely Look at Statistical Inference....Pages 3-15
Introduction to Learning Bayesian Networks from Data....Pages 17-57
A Casual View of Multi-Layer Perceptrons as Probability Models....Pages 59-80
Introduction to Statistical Phylogenetics....Pages 83-145
Detecting Recombination in DNA Sequence Alignments....Pages 147-190
RNA-Based Phylogenetic Methods....Pages 191-210
Statistical Methods in Microarray Gene Expression Data Analysis....Pages 211-238
Inferring Genetic Regulatory Networks from Microarray Experiments with Bayesian Networks....Pages 239-267
Modeling Genetic Regulatory Networks using Gene Expression Profiling and State-Space Models....Pages 269-293
An Anthology of Probabilistic Models for Medical Informatics....Pages 297-349
Bayesian Analysis of Population Pharmacokinetic/Pharmacodynamic Models....Pages 351-370
Assessing the Effectiveness of Bayesian Feature Selection....Pages 371-389
Bayes Consistent Classification of EEG Data by Approximate Marginalization....Pages 391-417
Ensemble Hidden Markov Models with Extended Observation Densities for Biosignal Analysis....Pages 419-450
A Probabilistic Network for Fusion of Data and Knowledge in Clinical Microbiology....Pages 451-472
Software for Probability Models in Medical Informatics....Pages 473-489
Back Matter....Pages 491-504
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