All About Bioinformatics: From Beginner to Expert provides readers with an overview of the fundamentals and advances in the field of bioinformatics, as well as some future directions. Each chapter is didactically organized and includes introduction, applications, tools, and future directions to cover the topics thoroughly.
The book covers both traditional topics such as biological databases, algorithms, genetic variations, static methods, and structural bioinformatics, as well as contemporary advanced topics such as high-throughput technologies, drug informatics, system and network biology, and machine learning. It is a valuable resource for researchers and graduate students who are interested to learn more about bioinformatics to apply in their research work.
1. Chapter 2 Introduction to biological databases - p. 19 2. Chapter 3 Statistical methods in bioinformatics - p. 43 3. Chapter 4 Algorithms in computational biology - p. 77 4. Chapter 5 Genetic variations - p. 105 5. Chapter 6 Structural bioinformatics - p. 135 6. Chapter 7 High throughput technology - p. 153 7. Chapter 8 Drug informatics - p. 179 8. Chapter 9 A machine learning approach to bioinformatics - p. 203 9. Chapter 10 Systems and network biology - p. 225 10. Chapter 11 Bioinformatics workflow management systems - p. 247 11. Chapter 12 Data handling using Python - p. 267 12. 12.1: Introduction - p. 267 13. 12.2: Datatypes and operators - p. 268 14. 12.3: Variables - p. 269 15. 12.4: Strings - p. 270 16. 12.5: Python lists and tuples - p. 273 17. 12.6: Dictionary in Python - p. 276 18. 12.7: Conditional statements - p. 277 19. 12.8: Loops in Python - p. 279 20. 12.9: File handling in Python - p. 281 21. 12.10: Importing functions - p. 283 22. 12.11: Data handling - p. 286 23. Index - p. 289
All About Bioinformatics: From Beginner to Expert provides readers with an overview of the fundamentals and advances in the field of bioinformatics, as well as some future directions. Each chapter is didactically organized and includes introduction, applications, tools, and future directions to cover the topics thoroughly.
The book covers both traditional topics such as biological databases, algorithms, genetic variations, static methods, and structural bioinformatics, as well as contemporary advanced topics such as high-throughput technologies, drug informatics, system and network biology, and machine learning. It is a valuable resource for researchers and graduate students who are interested to learn more about bioinformatics to apply in their research work.
1. Chapter 2 Introduction to biological databases - p. 19 2. Chapter 3 Statistical methods in bioinformatics - p. 43 3. Chapter 4 Algorithms in computational biology - p. 77 4. Chapter 5 Genetic variations - p. 105 5. Chapter 6 Structural bioinformatics - p. 135 6. Chapter 7 High throughput technology - p. 153 7. Chapter 8 Drug informatics - p. 179 8. Chapter 9 A machine learning approach to bioinformatics - p. 203 9. Chapter 10 Systems and network biology - p. 225 10. Chapter 11 Bioinformatics workflow management systems - p. 247 11. Chapter 12 Data handling using Python - p. 267 12. 12.1: Introduction - p. 267 13. 12.2: Datatypes and operators - p. 268 14. 12.3: Variables - p. 269 15. 12.4: Strings - p. 270 16. 12.5: Python lists and tuples - p. 273 17. 12.6: Dictionary in Python - p. 276 18. 12.7: Conditional statements - p. 277 19. 12.8: Loops in Python - p. 279 20. 12.9: File handling in Python - p. 281 21. 12.10: Importing functions - p. 283 22. 12.11: Data handling - p. 286 23. Index - p. 289
