Statistical Methods in Molecular Biology

  • Heejung Bang
  • Xi Kathy Zhou
  • Heather L. van Epps
  • Madhu Mazumdar

Part of the Methods in Molecular Biology book series (MIMB, volume 620)

Table of contents

  1. Front Matter
    Pages i-xiii
  2. Basic Statistics

    1. Front Matter
      Pages 1-1
    2. Heejung Bang, Marie Davidian
      Pages 1-102
    3. Knut M. Wittkowski, Tingting Song
      Pages 105-153
    4. Sujit K. Ghosh
      Pages 155-178
    5. Mithat Gönen
      Pages 179-199
  3. Designs and Methods for Molecular Biology

    1. Front Matter
      Pages 201-201
    2. Yuehua Cui, Gengxin Li, Shaoyu Li, Rongling Wu
      Pages 219-242
    3. Melissa J. Fazzari, John M. Greally
      Pages 243-265
  4. Statistical Methods for Microarray Data

    1. Front Matter
      Pages 285-285
    2. Martina Bremer, Edward Himelblau, Andreas Madlung
      Pages 287-313
    3. Bradley M. Broom, Waree Rinsurongkawong, Lajos Pusztai, Kim-Anh Do
      Pages 315-343
  5. Advanced or Specialized Methods for Molecular Biology

  6. Meta-Analysis for High-Dimensional Data

    1. Front Matter
      Pages 509-509
    2. Trecia A. Kippola, Stephanie A. Santorico
      Pages 541-560
  7. Other Practical Information

    1. Front Matter
      Pages 561-561
    2. Madhu Mazumdar, Samprit Banerjee, Heather L. Van Epps
      Pages 563-598
    3. Jennifer Sousa Brennan
      Pages 599-626
  8. Back Matter
    Pages 627-636

About this book


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


Microarray Proteomics Stata clustering data analysis gene expression genome hidden markov model molecular biology

Editors and affiliations

  • Heejung Bang
    • 1
  • Xi Kathy Zhou
    • 2
  • Heather L. van Epps
    • 3
  • Madhu Mazumdar
    • 4
  1. 1.Weill Medical College, Dept. Public HealthCornell UniversityNew YorkUSA
  2. 2.Weill Medical College, Dept. Public HealthCornell UniversityNew YorkUSA
  3. 3.Journal of Experimental MedicineRockefeller University PressNew YorkUSA
  4. 4.Weill Medical College, Dept. Public HealthCornell UniversityNew YorkUSA

Bibliographic information

  • DOI
  • Copyright Information Humana Press 2010
  • Publisher Name Humana Press, Totowa, NJ
  • eBook Packages Springer Protocols
  • Print ISBN 978-1-60761-578-1
  • Online ISBN 978-1-60761-580-4
  • Series Print ISSN 1064-3745
  • Series Online ISSN 1940-6029
  • Buy this book on publisher's site
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