Ulteriori informazioni
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.
Sommario
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.
Riassunto
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.
Testo aggiuntivo
From the reviews:
"This book is a collection of chapters describing methods of statistical analysis of medical and biological data, with a focus on mathematical descriptions and implementing algorithms. … It will be particularly useful for those who are interested in a better understanding of artificial neutral networks … . Generally, it is a refreshing book for a statistician … giving a good description of a wide variety of complex models." (Natalia Bochkina, Significance, Vol. 3 (3), 2006)
"This book covers recent advances in the use of probabilistic models in computational molecular biology, bioinformatics and biomedicine. … 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. The book has been written for researchers and students in statistics, informatics, and biological sciences … . Finally, an appendix explains the conventions and notation used throughout the book." (T. Postelnicu, Zentralblatt MATH, Vol. 1151, 2009)
Relazione
From the reviews:
"This book is a collection of chapters describing methods of statistical analysis of medical and biological data, with a focus on mathematical descriptions and implementing algorithms. ... It will be particularly useful for those who are interested in a better understanding of artificial neutral networks ... . Generally, it is a refreshing book for a statistician ... giving a good description of a wide variety of complex models." (Natalia Bochkina, Significance, Vol. 3 (3), 2006)
"This book covers recent advances in the use of probabilistic models in computational molecular biology, bioinformatics and biomedicine. ... 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. The book has been written for researchers and students in statistics, informatics, and biological sciences ... . Finally, an appendix explains the conventions and notation used throughout the book." (T. Postelnicu, Zentralblatt MATH, Vol. 1151, 2009)
