附註:Includes bibliographical references (pages 457-509).
Chapter 1. Molecular Biology for the Practical Bioinformatician -- Chapter 2. Strategy and Planning of Bioinformatics Experiments -- Chapter 3. DataMining Techniques for the Practical Bioinformatician -- Chapter 4. Techniques for Recognition of Translation Initiation Sites -- Chapter 5. How Neural Networks Find Promoters Using Recognition of Micro-Structural Promoter Components -- Chapter 6. Neural-StatisticalModel of TATA-Box Motifs in Eukaryotes -- Chapter 7. Tuning the Dragon Promoter Finder System for Human Promoter Recognition -- Chapter 8. RNA Secondary Structure Prediction -- Chapter 9. Protein Localization Prediction -- Chapter 10. Homology Search Methods -- Chapter 11. Analysis of Phylogeny: A Case Study on Saururaceae -- Chapter 12. Functional Annotation and Protein Families: From Theory to Practice -- Chapter 13. Discovering Protein-Protein Interactions -- Chapter 14. Techniques for Analysis of Gene Expression -- Chapter 15. Genome-Wide cDNA Oligo Probe Design and its Applications in Schizosaccharomyces Pombe -- Chapter 16. Mining New Motifs from cDNA Sequence Data -- Chapter 17. Technologies for Biological Data Integration -- Chapter 18. Construction of Biological Databases: A Case Study on the Protein Phosphatase DataBase (PPDB) -- Chapter 19. A Family Classification Approach to Functional Annotation of Proteins -- Chapter 20. Informatics for Efficient EST-Based Gene Discovery in Normalized and Subtracted cDNA Libraries.
摘要:Computer scientists have increasingly been enlisted as "bioinformaticians" to assist molecular biologists in their research. This book is a practical introduction to bioinformatics for these computer scientists. The chapters are in-depth discussions by expert bioinformaticians on both general techniques and specific approaches to a range of selected bioinformatics problems. The book is organized into clusters of chapters on the following topics:. Overview of modern molecular biology and a broad spectrum of techniques from computer science - data mining, machine learning, mathematical modeling.
