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Title | Accelerating the Original Profile Kernel. |
Publication Type | Journal Article |
Year of Publication | 2013 |
Authors | Hamp, T, Goldberg, T, Rost, B |
Journal | PLoS One |
Volume | 8 |
Issue | 6 |
Pagination | e68459 |
Date Published | 2013 |
ISSN | 1932-6203 |
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. |
DOI | 10.1371/journal.pone.0068459 |
Alternate Journal | PLoS ONE |
PubMed ID | 23825697 |
PubMed Central ID | PMC3688983 |