| Literature DB >> 17623703 |
Abstract
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.Entities:
Mesh:
Year: 2007 PMID: 17623703 DOI: 10.1093/bioinformatics/btm350
Source DB: PubMed Journal: Bioinformatics ISSN: 1367-4803 Impact factor: 6.937