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exclusively to Bayesian methods of analysis for applications to high-throughput gene expression data, exploring the relevant methods that are changing;
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phylogenic analysis, sparsity priors for protein-protein interaction predictions, and Bayesian networks for gene expression data. The text also;
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phylogenic analysis, sparsity priors for protein-protein interaction predictions, and Bayesian networks for gene expression data. The text also;
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of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as;
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of Bayesian methods in the analysis of high-throughput bioinformatics data that arise from medical, in particular, cancer research, as well as;
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patterns in gene expression microarray data sets* Sample-based analysis: supervised and unsupervised methods for the reduction of the gene;
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expression and on genetic association data and briefly covering metabolomics data and proteomics data * gradual introduction to the mathematical;
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combine advantages of traditional Bayesian and frequentist statistics. Techniques for estimating the local false discovery rate assign;
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for generating gene expression data. Background knowledge on statistical modeling and machine learning techniques. Detailed methodology;
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High-throughput measurements of gene expression and genetic marker data facilitate systems biologic and systems genetic data analysis;
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authors of this book. Statistical Analysis of Gene Expression Microarray Data promises to become the definitive basic reference in the field;
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In the post-genomic age, much biomedical research looks at when, where, and at what level genes are expressed. Measuring Gene Expression;
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literature.In this book the authors present Bayesian methods of analysis for nonlinear, hierarchical mixture models, with a finite, but possibly unknown;
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that are available from non--commercial sources. Concepts and principles underpinning gene expression analysis are emphasised and wherever;
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, Analysis of microarray gene expression data using information theory and stochastic algorithm, Hybrid Models, Markov Chain Monte Carlo Methods;
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apply Bioconductor tools in the analysis of microarray gene expression data. Topics covered include: (1) import and preprocessing of data from;
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A multi-discipline, hands-on guide to microarray analysis of biological processes Analyzing Microarray Gene Expression Data provides a;
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. It also covers differential gene expression detection as well as classification and cluster analysis using gene expression data sets;
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for Gene Regulation* Estimating Transcriptional Regulatory Networks by a Bayesian Network* Analysis of Therapeutic Compound Effects;
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, and simulation of regulatory gene networks, and the mapping of expression data to metabolic pathways and chromosome locations have been added;
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This volume provides experimental and bioinformatics approaches related to different aspects of gene expression analysis. Divided in three;
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Analyzing high-dimensional gene expression and DNA methylation data with R is the first practical book that shows a ``pipeline;
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Analyzing high-dimensional gene expression and DNA methylation data with R is the first practical book that shows a ``pipeline;
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a molecular biologist and a biostatistician with a combined decade of experience in practical expression profiling experiments and data;
Vergelijkbare producten zoals Gene Expression Studies Using Affymetrix Microarrays
a molecular biologist and a biostatistician with a combined decade of experience in practical expression profiling experiments and data;
Vergelijkbare producten zoals Gene Expression Studies Using Affymetrix Microarrays
Computational models have been playing a significant role for the computer-based analysis of biological and biomedical data. Given the;
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