@inproceedings{563f5794bf29426f9c4b2bd6e01d5e22,
title = "Adaptive locality-effective kernel machine for protein phosphorylation site prediction",
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.",
author = "Yoo, {Paul D.} and Yung, {Shwen Ho} and Bing, {Bing Zhou} and Zomaya, {Albert Y.}",
year = "2008",
doi = "10.1109/IPDPS.2008.4536173",
language = "English (US)",
isbn = "9781424416943",
series = "IPDPS Miami 2008 - Proceedings of the 22nd IEEE International Parallel and Distributed Processing Symposium, Program and CD-ROM",
booktitle = "IPDPS Miami 2008 - Proceedings of the 22nd IEEE International Parallel and Distributed Processing Symposium, Program and CD-ROM",
note = "IPDPS 2008 - 22nd IEEE International Parallel and Distributed Processing Symposium ; Conference date: 14-04-2008 Through 18-04-2008",
}