Author: Marina Vannucci
Edition: 1
Publisher: Cambridge University Press
Binding: Hardcover
ISBN: 052186092X
Bayesian Inference for Gene Expression and Proteomics
The interdisciplinary nature of bioinformatics presents a research challenge in integrating concepts, methods, software and multiplatform data. Medical books Bayesian Inference for Gene Expression and Proteomics. Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. This book discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical methods. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation is followed by expert reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and clustering, motif discovery and regulatory networks, and Bayesian networks and gene interactions Medical books Bayesian Inference for Gene Expression and Proteomics by Do, Kim-Anh/. Bayesian Inference for Gene Expression and Proteomics by Do, Kim-Anh/ Mueller, Peter/ Vannucci, Marina [Hardcover]
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Bayesian Inference for Gene Expression and Proteomics by Do, Kim-Anh/ Mueller, Peter/ Vannucci, Marina [Hardcover]
Bayesian Inference for Gene Expression And Proteomics , ISBN-13: 9780521860925, ISBN-10: 052186092X
Bayesian Inference for Gene Expression and Proteomics Ucmbs 9781107636989 09781107636989
The interdisciplinary nature of bioinformatics presents a research challenge in integrating concepts, methods, software and multiplatform data. Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. Bayesian Inference for Gene Expression and Proteomics discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research,
Medical Book Bayesian Inference for Gene Expression and Proteomics
Although there have been rapid developments in new technology and an inundation of statistical methods for addressing other types of high-throughput data, such as proteomic profiles that arise from mass spectrometry experiments. This book discusses the development and application of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as molecular and structural biology. The Bayesian approach has the advantage that evidence can be easily and flexibly incorporated into statistical methods. A basic overview of the biological and technical principles behind multi-platform high-throughput experimentation is followed by expert reviews of Bayesian methodology, tools and software for single group inference, group comparisons, classification and clustering, motif discovery and regulatory networks, and Bayesian networks and gene interactions.
