This article reviews machine learning methods for bioinformatics. It presents modelling
methods, such as supervised classification, clustering and probabilistic graphical models for …

A long standing problem in structural bioinformatics is to determine the three-dimensional (3-
D) structure of a protein when only a sequence of amino acid residues is given. Many …

A comprehensive update of the leading algorithms text, with new material on matchings in
bipartite graphs, online algorithms, machine learning, and other topics. Some books on …

The formalism of probabilistic graphical models provides a unifying framework for capturing
complex dependencies among random variables, and building large-scale multivariate …

For the last 20 years, fragment assembly in DNA sequencing followed the “overlap–layout–
consensus” paradigm that is used in all currently available assembly tools. Although this …

A guide to machine learning approaches and their application to the analysis of biological
data. An unprecedented wealth of data is being generated by genome sequencing projects …

Before there were computers, there were algorithms. But now that there are computers, there
are even more algorithms, and algorithms lie at the heart of computing. This book provides a …

Our basic question is: Given a collection of DNA sequences, what underlying forces are
responsible for the observed patterns of variability? To approach this question we introduce …

Recent years have witnessed a dramatic increase of interest in sophisticated string matching
problems, especially in information retrieval and computational biology. This book presents …

This second edition of the must-read work in the field presents generic computational
models and techniques that can be used for the development of evolving, adaptive modeling …