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Parallel and Distributed Processing Symposium, International (2008)
Miami, FL, USA
Apr. 14, 2008 to Apr. 18, 2008
ISBN: 978-1-4244-1693-6
pp: 1-8
Albert Y. Zomaya , Advanced Networks Research Group, School of Information Technologies (J12), University of Sydney, NSW 2006, Australia
Paul D. Yoo , Advanced Networks Research Group, School of Information Technologies (J12), University of Sydney, NSW 2006, Australia
Yung Shwen Ho , Advanced Networks Research Group, School of Information Technologies (J12), University of Sydney, NSW 2006, Australia
Bing Bing Zhou , Advanced Networks Research Group, School of Information Technologies (J12), University of Sydney, NSW 2006, Australia
ABSTRACT
In this study, we propose a new machine learning model namely, Adaptive Locality-Effective Kernel Machine (Adaptive-LEKM) for protein phosphorylation site prediction. Adaptive-LEKM proves to be more accurate and exhibits a much stable predictive performance over the existing machine learning models. Adaptive-LEKM is trained using Position Specific Scoring Matrix (PSSM) to detect possible protein phosphorylation sites for a target sequence. The performance of the proposed model was compared to seven existing different machine learning models on newly proposed PS-Benchmark_1 dataset in terms of accuracy, sensitivity, specificity and correlation coefficient. Adaptive-LEKM showed better predictive performance with 82.3% accuracy, 80.1% sensitivity, 84.5% specificity and 0.65 correlation-coefficient than contemporary machine learning models.
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CITATION
Albert Y. Zomaya, Paul D. Yoo, Yung Shwen Ho, Bing Bing Zhou, "Adaptive Locality-Effective Kernel Machine for protein phosphorylation site prediction", Parallel and Distributed Processing Symposium, International, vol. 00, no. , pp. 1-8, 2008, doi:10.1109/IPDPS.2008.4536173
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