Methods for Computational Gene Prediction (Paperback)

William H. Majoros

ISBN 10: 0521706947 / ISBN 13: 9780521706940

Published by Cambridge University Press, Cambridge, 2007

About this Item

Paperback. 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. This advanced text describes in detail the algorithms and models used to identify genes in genomic DNA sequences. It provides the underlying theory of both established techniques and also methods at the forefront of current research and is ideal for use in a first course in bioinformatics or computational biology. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Seller Inventory # 9780521706940

Bibliographic Details

• Title: Methods for Computational Gene Prediction (Paperback)
• Publisher: Cambridge University Press, Cambridge
• Publication Date: 2007
• Language: English
• ISBN 10: 0521706947
• ISBN 13: 9780521706940
• Binding: Paperback
• Edition: 1st Edition
• Condition: new

About this title

Synopsis

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.

About the Author

W. H. Majoros is Staff Scientist at the Center for Bioinformatics and Computational Biology, in the Institute for Genome Sciences and Policy at Duke University. He has worked as a research scientist in the fields of computational biology, natural language processing, and information retrieval for over a decade. He was part of the human genome project at Celera Genomics and has taken part in the sequencing and analysis of numerous organisms including human, mouse, fly and mosquito.