Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

UNLABELLED: Hidden Markov models are widely applied within computational biology. The large data sets and complex models involved demand optimized implementations, while efficient exploration of model space requires rapid prototyping. These requirements are not met by existing solutions, and hand-coding is time-consuming and error-prone. Here, I present a compiler that takes over the mechanical process of implementing HMM algorithms, by translating high-level XML descriptions into efficient C++ implementations. The compiler is highly customizable, produces efficient and bug-free code, and includes several optimizations. AVAILABILITY: http://genserv.anat.ox.ac.uk/software.

Original publication

DOI

10.1093/bioinformatics/btm350

Type

Journal article

Journal

Bioinformatics

Publication Date

15/09/2007

Volume

23

Pages

2485 - 2487

Keywords

Algorithms, Artificial Intelligence, Computational Biology, Computer Simulation, Markov Chains, Models, Biological, Models, Statistical, Pattern Recognition, Automated, Programming Languages, Sequence Analysis, Software