Statistical methods in molecular biology (Book, 2010) []
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Statistical methods in molecular biology

Statistical methods in molecular biology

Author: Heejung Bang
Publisher: New York : Humana Press, ©2010.
Series: Springer protocols (Series); Methods in molecular biology (Clifton, N.J.), v. 620.
Edition/Format:   Print book : EnglishView all editions and formats
While there is a wide selection of 'by experts, for experts' books in statistics and molecular biology, there is a distinct need for a book that presents the basic principles of proper statistical analyses and progresses to more advanced statistical methods in response to rapidly developing technologies and methodologies in the field of molecular biology. Statistical Methods in Molecular Biology strives to fill that  Read more...
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Genre/Form: Laboratory manuals
Laboratory Manual
Manuels de laboratoire
Material Type: Internet resource
Document Type: Book, Internet Resource
All Authors / Contributors: Heejung Bang
ISBN: 9781607615781 1607615789 9781607615804 1607615800
OCLC Number: 462919274
Description: xiii, 636 pages : illustrations
Contents: Part I. Basic statistics --
1. Experimental statistics for biological sciences / Heejung Bang and Marie Davidian --
2. Nonparametric methods for molecular biology / Knut M. Wittkowski and Tingting Song --
3. Basics of Bayesian methods / Sujit K. Ghosh --
4. The Bayesian t-test and beyond / Mithat Gönen --
Part II. Designs and methods for molecular biology --
5. Sample size and power calculation for molecular biology studies / Sin-Ho Jung --
6. Designs for linkage analysis and association studies of complex diseases / Yuehua Cui [and others] --
7. Introduction to epigenomics and epigenome-wide analysis / Melissa J. Fazzari and John M. Greally --
8. Exploration, visualization, and preprocessing of high-dimensional data / Zhijin Wu and Zhiqiang Wu --
Part III . Statistical methods for microarray data --
9. Introduction to the statistical analysis of two-color microarray data / Martina Bremer, Edward Himelblau, and Andreas Madlung --
10. Building networks with microarray data / Bradley M. Broom [and others] --
Part IV. Advanced or specialized methods for molecular biology --
11. Support vector machines for classification: a statistical portrait / Yoonkyung Lee --
12. An overview of clustering applied to molecular biology / Rebecca Nugent and Marina Meila --
13. Hidden Markov model and its applications in motif findings / Jing Wu and Jun Xie --
14. Dimension reduction for high-dimensional data / Lexin Li --
15. Introduction to the development and validation of predictive biomarker models from high-throughput data sets / Xutao Deng and Fabien Campagne --
16. Multi-gene expression-based statistical approaches to predicting --
patients' clinical outcomes and responses / Feng Cheng, Sang-Hoon Cho, and Jae K. Lee --
17. Two-stage testing strategies for genome-wide association studies in family-based designs / Amy Murphy, Scott T. Weiss, and Christoph Lange --
18. Statistical methods for proteomics / Klaus Jung --
Part V. Meta-analysis for high-dimensional data --
19. Statistical methods for integrating multiple types of high-throughput data / Yang Xie and Chul Ahn --
20. A Bayesian hierarchical model for high-dimensional meta-analysis / Fei Liu --
21. Methods for combining multiple genome-wide linkage studies / Trecia A. Kippola and Stephanie A. Santorico --
Part VI. Other practical information --
22. Improved reporting of statistical design and analysis: guidelines, education, and editorial policies / Madhu Mazumdar, Samprit Banerjee, and Heather L. Van Epps --
23. Stata companion / Jennifer Sousa Brennan.
Series Title: Springer protocols (Series); Methods in molecular biology (Clifton, N.J.), v. 620.
Responsibility: edited by Heejung Bang [and others].


This progressive book presents the basic principles of proper statistical analyses. It progresses to more advanced statistical methods in response to rapidly developing technologies and methodologies  Read more...


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Publisher Synopsis

"Here is a comprehensive book that systematically covers both basic and advanced statistical topics in molecular biology, including parametric and nonparametric, and frequentist and Bayesian methods. Read more...

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