Methods for Computational Gene Prediction - William H. Majoros

Methods for Computational Gene Prediction

Buch | Softcover
448 Seiten
2007
Cambridge University Press (Verlag)
978-0-521-70694-0 (ISBN)
52,35 inkl. MwSt
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.
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.

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.

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.

Erscheint lt. Verlag 16.8.2007
Zusatzinfo Worked examples or Exercises; 30 Tables, unspecified; 139 Line drawings, unspecified
Verlagsort Cambridge
Sprache englisch
Maße 175 x 247 mm
Gewicht 888 g
Themenwelt Informatik Weitere Themen Bioinformatik
Naturwissenschaften Biologie Genetik / Molekularbiologie
ISBN-10 0-521-70694-7 / 0521706947
ISBN-13 978-0-521-70694-0 / 9780521706940
Zustand Neuware
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