Adaptive locality-effective kernel machine for protein phosphorylation site prediction

Paul D. Yoo, Ho Yung Shwen, Bing Zhou Bing, Albert Y. Zomaya

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationIPDPS Miami 2008 - Proceedings of the 22nd IEEE International Parallel and Distributed Processing Symposium, Program and CD-ROM
DOIs
StatePublished - Sep 10 2008
EventIPDPS 2008 - 22nd IEEE International Parallel and Distributed Processing Symposium - Miami, FL, United States
Duration: Apr 14 2008Apr 18 2008

Publication series

NameIPDPS Miami 2008 - Proceedings of the 22nd IEEE International Parallel and Distributed Processing Symposium, Program and CD-ROM

Other

OtherIPDPS 2008 - 22nd IEEE International Parallel and Distributed Processing Symposium
CountryUnited States
CityMiami, FL
Period04/14/0804/18/08

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software
  • Electrical and Electronic Engineering

Cite this