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Methods for Computational Gene Prediction(ISBN=9780521706940)书籍详细信息
- ISBN：9780521706940
- 作者：暂无作者
- 出版社：暂无出版社
- 出版时间：2012-01
- 页数：430
- 价格：294.00
- 纸张：胶版纸
- 装帧：平装
- 开本：16开
- 语言：未知
- 丛书：暂无丛书
- TAG：暂无
- 豆瓣评分：暂无豆瓣评分
内容简介：
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.
书籍目录：
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.
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