Python Programming for Biology
eBook - PDF

Python Programming for Biology

Bioinformatics and Beyond

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Python Programming for Biology

Bioinformatics and Beyond

About this book

Do you have a biological question that could be readily answered by computational techniques, but little experience in programming? Do you want to learn more about the core techniques used in computational biology and bioinformatics? Written in an accessible style, this guide provides a foundation for both newcomers to computer programming and those interested in learning more about computational biology. The chapters guide the reader through: a complete beginners' course to programming in Python, with an introduction to computing jargon; descriptions of core bioinformatics methods with working Python examples; scientific computing techniques, including image analysis, statistics and machine learning. This book also functions as a language reference written in straightforward English, covering the most common Python language elements and a glossary of computing and biological terms. This title will teach undergraduates, postgraduates and professionals working in the life sciences how to program with Python, a powerful, flexible and easy-to-use language.

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Yes, you can access Python Programming for Biology by Tim J. Stevens,Wayne Boucher in PDF and/or ePUB format, as well as other popular books in Biological Sciences & Genetics & Genomics. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half-title
  3. Title page
  4. Copyright information
  5. Table of contents
  6. Preface
  7. Acknowledgements
  8. 1 Prologue
  9. 2 A beginners' guide
  10. 3 Python basics
  11. 4 Program control and logic
  12. 5 Functions
  13. 6 Files
  14. 7 Object orientation
  15. 8 Object data modelling
  16. 9 Mathematics
  17. 10 Coding tips
  18. 11 Biological sequences
  19. 12 Pairwise sequence alignments
  20. 13 Multiple-sequence alignments
  21. 14 Sequence variation and evolution
  22. 15 Macromolecular structures
  23. 16 Array data
  24. 17 High-throughput sequence analyses
  25. 18 Images
  26. 19 Signal processing
  27. 20 Databases
  28. 21 Probability
  29. 22 Statistics
  30. 23 Clustering and discrimination
  31. 24 Machine learning
  32. 25 Hard problems
  33. 26 Graphical interfaces
  34. 27 Improving speed
  35. Appendices
  36. Glossary
  37. Index
  38. Color Plates