Hidden Markov Genomics Asger Hobolth at North Carolina State University's Bioinformatics Research Center and his team used a hidden Markov model to estimate that chimps and humans diverged from a ...

Hidden Markov Models and Their Applications Publication Trend The graph below shows the total number of publications each year in Hidden Markov Models and Their Applications.

The website includes six modules that teach key concepts in bioinformatics: sequence alignment, database search, motif discovery, genome rearrangements, fragment assembly, and hidden Markov Models.

An enhanced bioinformatics tool incorporating the participation of molecular structure as well as sequence in protein DNA recognition is proposed and tested. Boltzmann probability models of ...

Abstract Wavelet and hidden Markov-based modeling frameworks were developed to better capture the nonstationarity and non-Gaussian characteristics of streamflow that linear models cannot.