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(TM) 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

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