Computational Molecular Biology: An Introduction

Recently molecular biology has undergone unprecedented development generating vast quantities of data needing sophisticated computational methods for analysis, processing and archiving. This requirement has given birth to the truly interdisciplinary field of computational biology, or bioinformatics, a subject reliant on both theoretical and practical contributions from statistics, mathematics, computer science and biology.
∗ Provides the background mathematics required to understand why certain algorithms work
∗ Guides the reader through probability theory, entropy and combinatorial optimization
∗ In–depth coverage of molecular biology and protein structure prediction
∗ Includes several less familiar algorithms such as DNA segmentation, quartet puzzling and DNA strand separation prediction
∗ Includes class tested exercises useful for self study
∗ Source code of programs available on a Web site
Primarily aimed at advanced undergraduate and graduate students from bioinformatics, computer science, statistics, mathematics and the biological sciences, this text will also interest researchers from these fields.

Spis treści:
Molecular Biology.
Math Primer.
Sequence Alignment.
All About Eve.
Hidden Markov Models.
Structure Prediction.
Appendices.
References.
Index.

Okładka tylna:
Recently molecular biology has undergone unprecedented development generating vast quantities of data needing sophisticated computational methods for analysis, processing and archiving. This requirement has given birth to the truly interdisciplinary field of computational biology, or bioinformatics, a subject reliant on both theoretical and practical contributions from statistics, mathematics, computer science and biology.
∗ Provides the background mathematics required to understand why certain algorithms work
∗ Guides the reader through probability t heory, entropy and combinatorial optimization
∗ In–depth coverage of molecular biology and protein structure prediction
∗ Includes several less familiar algorithms such as DNA segmentation, quartet puzzling and DNA strand separation prediction
∗ Includes class tested exercises useful for self study
∗ Source code of programs available on a Web site
Primarily aimed at advanced undergraduate and graduate students from bioinformatics, computer science, statistics, mathematics and the biological sciences, this text will also interest researchers from these fields.