Biological sequence analysis
Richard M. Durbin, Sean R. Eddy, Anders Krogh, Graeme Mitchison · 1998 · 356 pages
As an Amazon Associate we earn from qualifying purchases. Some book links are affiliate links; you pay the same price and we may earn a small commission.
About this book
Probabilistic models are becoming increasingly important in analysing the huge amount of data being produced by large-scale DNA-sequencing efforts such as the Human Genome Project. For example, hidden Markov models are used for analysing biological sequences, linguistic-grammar-based probabilistic models for identifying RNA secondary structure, and probabilistic evolutionary models for inferring phylogenies of sequences from different organisms. This book gives a unified, up-to-date and self-contained account, with a Bayesian slant, of such methods, and more generally to probabilistic methods of sequence analysis. Written by an interdisciplinary team of authors, it aims to be accessible to molecular biologists, computer scientists, and mathematicians with no formal knowledge of the other fields, and at the same time present the state-of-the-art in this new and highly important field.
Appears in these reading paths
Bioinformatics: books to analyze genomes and biological data
Related reading guides
Reader reviews
Ratings and notes from readers — tagged with how deep into the subject they were.
Loading reviews…