Tim J. Stevens, Wayne Boucher

Python Programming for Biology

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Cambridge University Press
• Seitenzahl: 722
• Erscheinungstermin: 5. November 2018
• Englisch
• Abmessung: 244mm x 170mm x 38mm
• Gewicht: 1421g
• ISBN-13: 9780521720090
• ISBN-10: 0521720095
• Artikelnr.: 41216802

Autorenporträt

Tim Stevens is Senior Investigator Scientist at the MRC Laboratory of Molecular Biology in Cambridge. He researches single-cell 3D genome architecture and provides computational biology oversight, development and training within the Cell Biology Division.

Inhaltsangabe

Preface; Acknowledgements; 1. Prologue; 2. Beginners' guide; 3. Python basics; 4. Program control and logic; 5. Functions; 6. Files; 7. Object orientation; 8. Object data modelling; 9. Mathematics; 10. Coding tips; 11. Biological sequences; 12. Pairwise sequence alignments; 13. Multiple sequence alignments; 14. Sequence variation and evolution; 15. Macromolecular structures; 16. Array data; 17. High-throughput sequence analyses; 18. Images; 19. Signal processing; 20. Databases; 21. Probability; 22. Statistics; 23. Clustering and discrimination; 24. Machine learning; 25. Hard problems; 26. Graphical interfaces; 27. Improving speed; Appendix 1: simplified language reference; Appendix 2: selected standard type methods and operations; Appendix 3: standard module highlights; Appendix 4: string formatting; Appendix 5: regular expressions; Appendix 6: further statistics; Glossary; Index.

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Preface
Acknowledgements
1. Prologue
2. Beginners' guide
3. Python basics
4. Program control and logic
5. Functions
6. Files
7. Object orientation
8. Object data modelling
9. Mathematics
10. Coding tips
11. Biological sequences
12. Pairwise sequence alignments
13. Multiple sequence alignments
14. Sequence variation and evolution
15. Macromolecular structures
16. Array data
17. High-throughput sequence analyses
18. Images
19. Signal processing
20. Databases
21. Probability
22. Statistics
23. Clustering and discrimination
24. Machine learning
25. Hard problems
26. Graphical interfaces
27. Improving speed
Appendix 1: simplified language reference
Appendix 2: selected standard type methods and operations
Appendix 3: standard module highlights
Appendix 4: string formatting
Appendix 5: regular expressions
Appendix 6: further statistics
Glossary
Index.

Rezensionen

'As a long-time advocate of Python as the language of choice for both the bulk of biological data analysis and for teaching computer programming to molecular life scientists, I am delighted to see this book. [It] provides a well-focused introduction to Python programming but then goes on to use the clarity of the Python language to demystify a wide range of commonly applied data processing and analysis techniques that arise in modern cell and molecular biology. The integration of straightforward introductions to sequence analysis, image processing and statistical analysis (amongst others) into a book on Python is inspired. The clarity of the Python language helps to show that often 'yes it really is that simple', in a way that staring at a mathematical expression often fails to, and the programming element allows the reader to become actively involved.' Jeremy Craven, University of Sheffield