Methods for Computational Gene Prediction(ISBN=9780521706940) rb kindle 网盘 umd 极速 下载 pdf mobi

　　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.

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.

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书籍介绍

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.