Cambridge Catalogue  
  • Help
Home > Catalogue > Methods for Computational Gene Prediction
Methods for Computational Gene Prediction

Resources and solutions

This title has free online support material available.


  • 139 b/w illus. 30 tables 263 exercises
  • Page extent: 448 pages
  • Size: 247 x 175 mm
  • Weight: 0.888 kg


 (ISBN-13: 9780521706940)

Inferring the precise locations and splicing patterns of genes in DNA is a difficult but important task, with broad applications to biomedicine. The mathematical and statistical techniques that have been applied to this problem are surveyed and organized into a logical framework based on the theory of parsing. Both established approaches and methods at the forefront of current research are discussed. Numerous case studies of existing software systems are provided, in addition to detailed examples that work through the actual implementation of effective gene-predictors using hidden Markov models and other machine-learning techniques. Background material on probability theory, discrete mathematics, computer science, and molecular biology is provided, making the book accessible to students and researchers from across the life and computational sciences. This book is ideal for use in a first course in bioinformatics at graduate or advanced undergraduate level, and for anyone wanting to keep pace with this rapidly-advancing field.

• A self-contained text with all necessary background information provided to understand the material for students lacking expertise in statistics, computational science or molecular biology • Highly detailed, including both theory and practical advice to enable teachers to implement their own gene-finding software • Contains case studies of the most recent systems and published research to provide a timely picture of the current state-of-the art techniques in this rapidly-advancing field


Foreword Steven Salzberg; 1. Introduction; 2. Mathematical preliminaries; 3. Overview of gene prediction; 4. Gene finder evaluation; 5. A toy Exon finder; 6. Hidden Markov models; 7. Signal and content sensors; 8. Generalized hidden Markov models; 9. Comparative gene finding; 10. Machine Learning methods; 11. Tips and tricks; 12. Advanced topics; Appendix - online resources; References; Index.

printer iconPrinter friendly version AddThis