ArticlePloS one2013
Accelerating the Original Profile Kernel.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Protein embeddings and deep learning predict binding residues for various ligand classes.Scientific reports · 2021Article
- PredictProtein - Predicting Protein Structure and Function for 29 Years.Nucleic acids research · 2021Article
- Combining learning and constraints for genome-wide protein annotation.BMC bioinformatics · 2019Article
- Detailed prediction of protein sub-nuclear localization.BMC bioinformatics · 2019Article
- Computational prediction shines light on type III secretion origins.Scientific reports · 2016Article
- LocTree3 prediction of localization.Nucleic acids research · 2014Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.