Bayesian Analysis of Gene Expression Data (Statistics in Practice)

Description

The field of high-throughput genetic experimentation is evolving rapidly, with the advent of new technologies and new venues for data mining. Bayesian methods play a role central to the future of data and knowledge integration in the field of Bioinformatics. This book is devoted exclusively to Bayesian methods of analysis for applications to high-throughput gene expression data, exploring the relevant methods that are changing Bioinformatics. Case studies, illustrating Bayesian analyses of public gene expression data, provide the backdrop for students to develop analytical skills, while the more experienced readers will find the review of advanced methods challenging and attainable.

This book:

* Introduces the fundamentals in Bayesian methods of analysis for applications to high-throughput gene expression data.
* Provides an extensive review of Bayesian analysis and advanced topics for Bioinformatics, including examples that extensively detail the necessary applications.
* Accompanied by website featuring datasets, exercises and solutions.

Bayesian Analysis of Gene Expression Data offers a unique introduction to both Bayesian analysis and gene expression, aimed at graduate students in Statistics, Biomedical Engineers, Computer Scientists, Biostatisticians, Statistical Geneticists, Computational Biologists, applied Mathematicians and Medical consultants working in genomics. Bioinformatics researchers from many fields will find much value in this book.

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1. Ebooks list page : 3452
2. The Analysis of Gene Expression Data
3. Microarray Gene Expression Data Analysis
4. Analysis of Microarray Gene Expression Data (Trends in Logic S.)
5. DNA Microarrays and Gene Expression: From Experiments to Data Analysis and Modeling
6. Advanced Analysis of Gene Expression Microarray Data (2006)
7. Statistical Analysis of Gene Expression Microarray Data by Terry Speed
8. Statistical Analysis of Gene Expression Microarray Data by Terry Speed
9. Advanced Analysis of Gene Expression Microarray Data (Science, Engineering, and Biology Informatics)
10. Missing Data in Longitudinal Studies: Strategies for Bayesian Modeling and Sensitivity Analysis (Monographs on Statistics and
11. Analyzing Microarray Gene Expression Data
12. Analyzing Microarray Gene Expression Data (2004)
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