Statistical Methods in Molecular Biology
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 molecularbiology, 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

1127754225
Statistical Methods in Molecular Biology
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 molecularbiology, 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

199.99 In Stock
Statistical Methods in Molecular Biology

Statistical Methods in Molecular Biology

Overview

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 molecularbiology, 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

Product Details

ISBN-13: 9781493961245
Publisher: Springer-Verlag New York, LLC
Publication date: 08/23/2016
Series: Methods in Molecular Biology , #620
Edition description: Softcover reprint of the original 1st ed. 2010
Pages: 636
Product dimensions: 7.01(w) x 10.00(h) x (d)

Table of Contents

Basic Statistics.- Experimental Statistics for Biological Sciences.- Nonparametric Methods for Molecular Biology.- Basics of Bayesian Methods.- The Bayesian t-Test and Beyond.- Designs and Methods for Molecular Biology.- Sample Size and Power Calculation for Molecular Biology Studies.- Designs for Linkage Analysis and Association Studies of Complex Diseases.- to Epigenomics and Epigenome-Wide Analysis.- Exploration, Visualization, and Preprocessing of High–Dimensional Data.- Statistical Methods for Microarray Data.- to the Statistical Analysis of Two-Color Microarray Data.- Building Networks with Microarray Data.- Advanced or Specialized Methods for Molecular Biology.- Support Vector Machines for Classification: A Statistical Portrait.- An Overview of Clustering Applied to Molecular Biology.- Hidden Markov Model and Its Applications in Motif Findings.- Dimension Reduction for High-Dimensional Data.- to the Development and Validation of Predictive Biomarker Models from High-Throughput Data Sets.- Multi-gene Expression-based Statistical Approaches to Predicting Patients’ Clinical Outcomes and Responses.- Two-Stage Testing Strategies for Genome-Wide Association Studies in Family-Based Designs.- Statistical Methods for Proteomics.- Meta-Analysis for High-Dimensional Data.- Statistical Methods for Integrating Multiple Types of High-Throughput Data.- A Bayesian Hierarchical Model for High-Dimensional Meta-analysis.- Methods for Combining Multiple Genome-Wide Linkage Studies.- Other Practical Information.- Improved Reporting of Statistical Design and Analysis: Guidelines, Education, and Editorial Policies.- Stata Companion.
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