The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments.

The Handbook of Statistics, a series of self-contained reference books. Each volume is devoted to a particular topic in statistics with Volume 28 dealing with bioinformatics. Every chapter is written by prominent workers in the area to which the volume is devoted. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience.

Key Features

  • Comprehensively presents the various aspects of statistical methodology
  • Discusses a wide variety of diverse applications and recent developments
  • Contributors are internationally renowned experts in their respective areas


Statisticians and scientists in various disciplines who use statistical methodology in their work

Table of Contents

Editorial Board



Introduction: Wither Bioinformatics in Human Health and Heredity

1 Introduction

2 Sciences dealing with biological information and rationale of their integration

3 Goals and major research areas of bioinformatics

4 Why bioinformatics is so important and open areas of research

Bayesian Methods for Microarray Data

1 Introduction

2 Literature review

3 Hierarchical models for microarray analysis

4 Embryonic maize tissue development

5 Conclusion

6 Appendix

Statistical Analysis of Gene Expression Studies with Ordered Experimental Conditions

1 Introduction

2 “Short-series” time-course data

3 “Long series” time-course data for cyclic and developmental processes

4 Concluding remarks

Meta-Analysis of High Throughput Oncology Data

1 Introduction

2 Bayesian Networks

3 Example application in genetic epidemiology

4 Software and applications

5 Summary and future directions

A Statistical Appraisal of Biomarker Selection Methods Applicable to HIV/AIDS Research

1 Introduction

2 Biomarker definitions

3 HIV infection biomarker review

4 Statistical screening methods for biomarker selection

5 Causal inference approaches for biomarker selection

6 Targeted maximum likelihood estimation

7 Classifier performance assessed by ROC curve

8 Some impending statistical challenges

9 Multiplicity considerations in biomarker research

10 An application: hormonal contraception and HIV genital shedding and disease progression (GS study)

11 Discussion and conclusion

The Use of Hamming Distance in Bioinformatics

1 Introduction

2 Some diversity measures

3 U-statistics representation for the Hamming distance based measures in bioi


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© 2012
North Holland
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