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MORE ABOUT THIS BOOK
Feature:
An easily accessible reference for statistical methods in molecular miology, written by leading researchers in the field
Presents a comprehensive guide to self-learning analysis tools for data generated in molecular biology studies, from basic methods to advanced, specialized methods in a progressive style
Details the processing, description/visualization, and analyses of the data and software implementation
Covers a wide range of statistical methods for the analyses of various types of data collected in different fields of biological sciences, including standard experimental data and high-dimensional data
Back cover:
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."
- Robert C. Elston, PhD., Director, Division of Genetic and Molecular Epidemiology, Case Western Reserve University
"An extraordinary exposition of the central topics of modern molecular biology, presented by practicing experts who weave together rigorous theory with practical techniques and illustrative examples."
- George C. Newman, MD, PhD, Chairman, Neurosensory Sciences, Albert Einstein Medical Center
"I cannot think of anything we need now in translation research field more than more efficient cross talk between molecular biology and statistics. This book is just on target. It fills the gap."
- Iman Osman, MB, BCh, MD, Director, Interdisciplinary Melanoma Cooperative Program, New York University Langone Medical Center
Contents:
Part I: Basic Statistics
1. Experimental Statistics for Biological Sciences
Heejung Bang and Marie Davidian
2. Nonparametric Methods in 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, Gengxin Li, Shaoyu Li, and Rongling Wu
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, Waree Rinsurongkawong, Lajos Pusztai, and Kim-Anh Do
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 Datasets
Xutao Deng and Fabien Campagne
16. Multi-GeneExpression-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
PRODUCT DETAILS
Publisher: Springer (Humana Press)
Publication date: March, 2010
Pages: 636
Weight: 2950g
Availability: Not available (reason unspecified)
Subcategories: Biochemistry
From the same series
CUSTOMER REVIEWS
"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." (Robert C. Elston, PhD., Director, Division of Genetic and Molecular Epidemiology, Case Western Reserve University)
"An extraordinary exposition of the central topics of modern molecular biology, presented by practicing experts who weave together rigorous theory with practical techniques and illustrative examples." (George C. Newman, MD, PhD, Chairman, Neurosensory Sciences, Albert Einstein Medical Center)
"I cannot think of anything we need now in translation research field more than more efficient cross talk between molecular biology and statistics. This book is just on target. It fills the gap." (Iman Osman, MB, BCh, MD, Director, Interdisciplinary Melanoma Cooperative Program, New York University Langone Medical Center)