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Methods for computational gene prediction

Author: William H Majoros
Publisher: Cambridge ; New York : Cambridge University Press, 2007.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Database:WorldCat
Summary:
"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 here 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  Read more...
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Genre/Form: Electronic books
Case studies
Additional Physical Format: Print version:
Majoros, William H.
Methods for computational gene prediction.
Cambridge ; New York : Cambridge University Press, 2007
(DLC) 2007299649
(OCoLC)137221654
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: William H Majoros
ISBN: 9780511811135 0511811136
OCLC Number: 681447818
Reproduction Notes: Electronic reproduction. [S.l.] : HathiTrust Digital Library, 2010. MiAaHDL
Description: 1 online resource (xvii, 430 pages) : illustrations
Details: Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.
Contents: 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.
Responsibility: William H. Majoros.
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Abstract:

A self-contained, rigorous text describing models used to identify genes in genomic DNA sequences.  Read more...

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   schema:reviewBody ""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 here 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 sciences 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."--Jacket." ;
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