Advertisement

Statistical Methods in Bioinformatics

An Introduction

  • Warren J. Ewens
  • Gregory R. Grant

Part of the Statistics for Biology and Health book series (SBH)

Table of contents

  1. Front Matter
    Pages i-xix
  2. Warren J. Ewens, Gregory R. Grant
    Pages 1-54
  3. Warren J. Ewens, Gregory R. Grant
    Pages 55-104
  4. Warren J. Ewens, Gregory R. Grant
    Pages 105-127
  5. Warren J. Ewens, Gregory R. Grant
    Pages 129-145
  6. Warren J. Ewens, Gregory R. Grant
    Pages 147-180
  7. Warren J. Ewens, Gregory R. Grant
    Pages 181-217
  8. Warren J. Ewens, Gregory R. Grant
    Pages 219-236
  9. Warren J. Ewens, Gregory R. Grant
    Pages 237-267
  10. Warren J. Ewens, Gregory R. Grant
    Pages 269-302
  11. Warren J. Ewens, Gregory R. Grant
    Pages 303-325
  12. Warren J. Ewens, Gregory R. Grant
    Pages 327-347
  13. Warren J. Ewens, Gregory R. Grant
    Pages 349-363
  14. Warren J. Ewens, Gregory R. Grant
    Pages 365-384
  15. Warren J. Ewens, Gregory R. Grant
    Pages 385-422
  16. Back Matter
    Pages 423-476

About this book

Introduction

Advances in computers and biotechnology have had an immense impact on the biomedical fields, with broad consequences for humanity. Correspondingly, new areas of probability and statistics are being developed specifically to meet the needs of this area. There is now a necessity for a text that introduces probability and statistics in the bioinformatics context. This book also describes some of the main statistical applications in the field, including BLAST, gene finding, and evolutionary inference, much of which has not yet been summarized in an introductory textbook format.
This book grew out of the bioinformatics courses given at the University of Pennsylvania. The material is, however, organized to appeal to biologists or computer scientists who wish to know more about the statistical methods of the field, as well as to trained statisticians who wish to become involved in bioinformatics. The earlier chapters introduce the concepts of probability and statistics at an elementary level. Later chapters should be immediately accessible to the trained statistician. Sufficient mathematics background consists of courses in calculus and linear algebra. The basic biological concepts that are used are explained, or can be understood from the context.

Keywords

BLAST BLAST-Algorithmus DNA Markov chain Random variable bioinformatics biology computational biology evolution hidden markov model mathematics protein

Authors and affiliations

  • Warren J. Ewens
    • 1
  • Gregory R. Grant
    • 2
  1. 1.Department of BiologyUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Penn Center for Computational BiologyUniversity of PennsylvaniaPhiladelphiaUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4757-3247-4
  • Copyright Information Springer-Verlag New York 2001
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4757-3249-8
  • Online ISBN 978-1-4757-3247-4
  • Series Print ISSN 1431-8776
  • Buy this book on publisher's site
Industry Sectors
Pharma
Materials & Steel
Health & Hospitals
Biotechnology
IT & Software
Consumer Packaged Goods