Methods for computational gene prediction - Info and Reading Options
By William H. Majoros
"Methods for computational gene prediction" was published by Cambridge University Press in 2007 - Cambridge, the book is classified in bibliography genre, it has 430 pages and the language of the book is English.
“Methods for computational gene prediction” Metadata:
- Title: ➤ Methods for computational gene prediction
- Author: William H. Majoros
- Language: English
- Number of Pages: 430
- Publisher: Cambridge University Press
- Publish Date: 2007
- Publish Location: Cambridge
- Genres: bibliography - Case studies
- Dewey Decimal Classification: 572.860285
- Library of Congress Classification: QH447 .M35 2007QH438.4.M3
“Methods for computational gene prediction” Subjects and Themes:
- Subjects: ➤ Bioinformatics - Case studies - Data processing - Genomics - Mathematics - Molecular genetics - Methods - Computational Biology - Bioinformatik - Molekulare Bioinformatik - Genanalyse - Genetics, technique - Dna - Markov processes - methods
Edition Specifications:
- Number of Pages: xvii, 430 p. : ill. ; 26 cm.
- Pagination: xvii, 430 p. :
Edition Identifiers:
- The Open Library ID: OL18731422M - OL8332736W
- Online Computer Library Center (OCLC) ID: 137221654
- Library of Congress Control Number (LCCN): 2007299649 - ^^2007299649
- ISBN-13: 9780521877510 - 9780521706940 - 9780511811135
- All ISBNs: 9780521877510 - 9780521706940 - 0521877512 - 0521706947 - 9780511811135
AI-generated Review of “Methods for computational gene prediction”:
"Methods for computational gene prediction" Table Of Contents:
- 1- 1. Introduction
- 2- 2. Mathematical preliminaries
- 3- 3. Overview of gene prediction
- 4- 4. Gene finder evaluation
- 5- 5. A toy Exon finder
- 6- 6. Hidden Markov models
- 7- 7. Signal and content sensors
- 8- 8. Generalized hidden Markov models
- 9- 9. Comparative gene finding
- 10- 10. Machine Learning methods
- 11- 11. Tips and tricks
- 12- 12. Advanced topics.
"Methods for computational gene prediction" Description:
Harvard Library:
"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.
Open Data:
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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- Harvard University Library: Location: Ernst Mayr Library of the Museum of Comparative Zoology, Harvard University - Shelf Numbers: QH447 .M35 2007
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