Background: Hidden Markov Models (HMMs) have proven very useful in computational biology for such applications as sequence pattern matching, gene-finding, and structure prediction...
We introduce a novel framework for simultaneous structure and parameter learning in hidden-variable conditional probability models, based on an entropic prior and a solution for i...
Given concentrations of metabolites over a sequence of time steps, the metabolic pathway prediction problem seeks a set of reactions and rate constants for them that could yield t...
We describe a novel method for detecting the domain structure of a protein from sequence information alone. The method is based on analyzing multiple sequence alignments that are ...
Background: The emerging field of integrative bioinformatics provides the tools to organize and systematically analyze vast amounts of highly diverse biological data and thus allo...
Markus Fischer, Quan K. Thai, Melanie Grieb, J&uum...