DocumentCode :
578127
Title :
Meta-prediction of phosphorylation sites with multiplicative weighted update algorithms
Author :
Chen, Zih-yin ; Lu, Wei-fu
Author_Institution :
Dept. of Biomed. Sci., Asia Univ., Taichung, Taiwan
Volume :
2
fYear :
2012
fDate :
15-17 July 2012
Firstpage :
501
Lastpage :
506
Abstract :
There are numerous predictors have been developed to the phosphorylation sites prediction. However, there are no developed prediction programs that could make more accurate prediction than other prediction programs in every situation. Wan et al. [20] proposed meta-prediction strategies that integrate results of several prediction tools for phosphorylation sites prediction. Their meta-predictor gained an outstanding prediction performance that surpasses that of all combined prediction programs. They performed a generalized weighted voting strategy with parameters determined by restricted grid search to produce meta-prediction programs. Unfortunately, restricted grid search is time-consuming and the values of restricted grids should be computed using combinatorial analysis. In this paper, we make use of multiplicative update algorithms to learn better parameters for meta-predictions. The experimental results show that the proposed meta-predictor performs better than Wan´s meta-predictors, KinasePhos, KinasePhos 2.0, PPSP, GPS, NetPhosK and AMS 3.0 for SIT kinase families, PKA, PKC, CDK, and CK2.
Keywords :
biochemistry; biology computing; enzymes; learning (artificial intelligence); molecular biophysics; AMS 3.0; CDK; CK2; GPS; KinasePhos; KinasePhos 2.0; NetPhosK; PKA; PKC; PPSP; S/T kinase families; Wan´s meta-predictors; combinatorial analysis computing; generalized weighted voting strategy; machine learning; multiplicative update algorithms; multiplicative weighted update algorithms; phosphorylation site metaprediction; restricted grid search; Abstracts; Global Positioning System; Machine learning algorithms; Physiology; Prediction algorithms; Tin; Machine learning; Multiplicative algorithms; On-line decision problem; Phosphorylation sites prediction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Machine Learning and Cybernetics (ICMLC), 2012 International Conference on
Conference_Location :
Xian
ISSN :
2160-133X
Print_ISBN :
978-1-4673-1484-8
Type :
conf
DOI :
10.1109/ICMLC.2012.6358974
Filename :
6358974
Link To Document :
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