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Probabilistic prediction of protein phosphorylation sites using classification relevance units machines

Content Provider	ACM Digital Library
Author	Menor, Mark Baek, Kyungim Poisson, Guylaine
Abstract	Phosphorylation is an important post-translational modification of proteins that is essential to the regulation of many cellular processes. Although most of the phosphorylation sites discovered in protein sequences have been identified experimentally, the in vivo and in vitro discovery of the sites is an expensive, time-consuming and laborious task. Therefore, the development of computational methods for prediction of protein phosphorylation sites has drawn considerable attention. In this work, we present a kernel-based probabilistic Classification Relevance Units Machine (CRUM) for in silico phosphorylation site prediction. In comparison with the popular Support Vector Machine (SVM) CRUM shows comparable predictive performance and yet provides a more parsimonious model. This is desirable since it leads to a reduction in prediction run-time, which is important in predictions on large-scale data. Furthermore, the CRUM training algorithm has lower run-time and memory complexity and has a simpler parameter selection scheme than the Relevance Vector Machine (RVM) learning algorithm. To further investigate the viability of using CRUM in phosphorylation site prediction, we construct multiple CRUM predictors using different combinations of three phosphorylation site features -- BLOSUM encoding, disorder, and amino acid composition. The predictors are evaluated through cross-validation and the results show that CRUM with BLOSUM feature is among the best performing CRUM predictors in both cross-validation and benchmark experiments. A comparative study with existing prediction tools in an independent benchmark experiment suggests possible direction for further improving the predictive performance of CRUM predictors.
Starting Page	8
Ending Page	20
Page Count	13
File Format	PDF
ISSN	15596915 19310161
DOI	10.1145/2432546.2432547
Journal	ACM SIGAPP Applied Computing Review (SIAP)
Volume Number	12
Issue Number	4
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-06-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Phosphorylation Classification Kernel machine
Content Type	Text
Resource Type	Article

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