Predicting Subcellular Localization with AdaBoost Learner

Wen-Cong      Lu; Yu-huan      Jin; Bing      Niu; Kai-Yan      Feng; Yu-Dong      Cai; Guo-Zheng      Li

doi:10.2174/092986608783744234

Abstract

Protein subcellular localization, which tells where a protein resides in a cell, is an important characteristic of a protein, and relates closely to the function of proteins. The prediction of their subcellular localization plays an important role in the prediction of protein function, genome annotation and drug design. Therefore, it is an important and challenging role to predict subcellular localization using bio-informatics approach. In this paper, a robust predictor, AdaBoost Learner is introduced to predict protein subcellular localization based on its amino acid composition. Jackknife crossvalidation and independent dataset test were used to demonstrate that Adaboost is a robust and efficient model in predicting protein subcellular localization. As a result, the correct prediction rates were 74.98% and 80.12% for the Jackknife test and independent dataset test respectively, which are higher than using other existing predictors. An online server for predicting subcellular localization of proteins based on AdaBoost classifier was available on http://chemdata.shu. edu.cn/sl12.

Keywords: AdaBoost, subcellular localization, amino acid composition, jackknife cross-validation, independent dataset test

« Previous Next »