Biological Sequence Analysis

Ranked #17 in Bioinformatics

Probablistic models are becoming increasingly important in analyzing 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 analyzing 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... more

Similar Books

If you like Biological Sequence Analysis, check out these similar top-rated books:

Learn: What makes Shortform summaries the best in the world?