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.
目錄:
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.