title: HMMoC--a compiler for hidden Markov models.
abstract: 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.
  
  
  BACKGROUND
  http://genserv.anat.ox.ac.uk/software.
authors.affiliation: MRC Functional Genetics Unit, Department of Physiology, Anatomy and Genetics, South Parks Road, OX1 3TG, University of Oxford, UK. gerton.lunter@dpag.ox.ac.uk
authors.forename: Gerton
authors.initials: G
authors.lastname: Lunter
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D008390:Markov Chains; D008954:Models, Biological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D011381:Programming Languages; D017421:Sequence Analysis / Q000379:methods*; D012984:Software