title: Accelerating the Original Profile Kernel.
abstract: One of the most accurate multi-class protein classification systems continues to be the profile-based SVM kernel introduced by the Leslie group. Unfortunately, its CPU requirements render it too slow for practical applications of large-scale classification tasks. Here, we introduce several software improvements that enable significant acceleration. Using various non-redundant data sets, we demonstrate that our new implementation reaches a maximal speed-up as high as 14-fold for calculating the same kernel matrix. Some predictions are over 200 times faster and render the kernel as possibly the top contender in a low ratio of speed/performance. Additionally, we explain how to parallelize various computations and provide an integrative program that reduces creating a production-quality classifier to a single program call. The new implementation is available as a Debian package under a free academic license and does not depend on commercial software. For non-Debian based distributions, the source package ships with a traditional Makefile-based installer. Download and installation instructions can be found at https://rostlab.org/owiki/index.php/Fast_Profile_Kernel. Bugs and other issues may be reported at https://rostlab.org/bugzilla3/enter_bug.cgi?product=fastprofkernel.
authors.affiliation: Bioinformatics & Computational Biology - I12, Department of Informatics, Technical University of Munich, Garching/Munich, Germany.
authors.forename: Tobias
authors.initials: T
authors.lastname: Hamp
authors.forename: Tatyana
authors.initials: T
authors.lastname: Goldberg
authors.forename: Burkhard
authors.initials: B
authors.lastname: Rost
mesh_terms: D000465:Algorithms; D060388:Support Vector Machine