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Statistical methods in molecular biology / edited by Heejung Bang Xi Kathy Zhou, and Madhu Mazumdar.

Contributor(s): Bang, Heejung, 1972- [editor] | Zhou, Xi Kathy [editor] | Vans Epps, Heather L [editor] | Mazumdar, Madhu [editor].
Material type: materialTypeLabelBookSeries: Springer protocols; Methods in molecular biology 620.Copyright date: New York : Humana Press, 2010Edition: First Edition.Description: xiii, 636 pages : illustrations ; 27 cm.ISBN: 9781607615781; 1607615789 (alk. paper); 9781607615804 (eISBN); 1607615800 (eISBN).Subject(s): Molecular biology -- Statistical methods | Statistics | Bioinformatics | Life sciences | Biología molecular -- Métodos de estadística | Estadística | Bioinformática | Ciencias de la VidaDDC classification: 572.8015195 Other classification: WC 4150 | WC 7600 | WC 7700 Online resources: Inhaltsverzeichnis
Partial 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 ... [et al.] -- 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 ... [et al.] -- 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.
Abstract: 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 gap by covering basic and intermediate statistics that are useful for classical molecular biology settings and advanced statistical techniques that can be used to help solve problems commonly encountered in modern molecular biology studies, such as supervised and unsupervised learning, hidden Markov models, methods for manipulation and analysis of high-throughput microarray and proteomic data, and methods for the synthesis of the available evidences. This detailed volume offers molecular biologists a book in a progressive style where basic statistical methods are introduced and gradually elevated to an intermediate level, while providing statisticians knowledge of various biological data generated from the field of molecular biology, the types of questions of interest to molecular biologists, and the state-of-the-art statistical approaches to analyzing the data. As a volume in the highly successful Methods in Molecular Biology series, this work provides the kind of meticulous descriptions and implementation advice for diverse topics that are crucial for getting optimal results. Comprehensive but convenient, Statistical Methods in Molecular Biology will aid students, scientists, and researchers along the pathway from beginning strategies to a deeper understanding of these vital systems of data analysis and interpretation within one concise volume. "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. I am highly impressed by the breadth and depth of the applications. I strongly recommend this book for both statisticians and biologists who need to communicate with each other in this exciting field of research.
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Item type Current location Call number Copy number Status Date due Item holds
Libro académico Libro académico Biblioteca del Campus
572.8015195 S79774 2010 (Browse shelf) Ej. 1 Available
Total holds: 0

Includes bibliographical references and index.

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 ... [et al.] -- 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 ... [et al.] -- 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.

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 gap by covering basic and intermediate statistics that are useful for classical molecular biology settings and advanced statistical techniques that can be used to help solve problems commonly encountered in modern molecular biology studies, such as supervised and unsupervised learning, hidden Markov models, methods for manipulation and analysis of high-throughput microarray and proteomic data, and methods for the synthesis of the available evidences. This detailed volume offers molecular biologists a book in a progressive style where basic statistical methods are introduced and gradually elevated to an intermediate level, while providing statisticians knowledge of various biological data generated from the field of molecular biology, the types of questions of interest to molecular biologists, and the state-of-the-art statistical approaches to analyzing the data. As a volume in the highly successful Methods in Molecular Biology series, this work provides the kind of meticulous descriptions and implementation advice for diverse topics that are crucial for getting optimal results. Comprehensive but convenient, Statistical Methods in Molecular Biology will aid students, scientists, and researchers along the pathway from beginning strategies to a deeper understanding of these vital systems of data analysis and interpretation within one concise volume. "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. I am highly impressed by the breadth and depth of the applications. I strongly recommend this book for both statisticians and biologists who need to communicate with each other in this exciting field of research.

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