title: Predicting subcellular localization with AdaBoost Learner.
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 cross-validation 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.
authors.affiliation: Department of Chemistry, College of Sciences, Shanghai University, 99 Shang-Da Road, Shanghai, China 200444.
authors.forename: Yu-Huan
authors.initials: YH
authors.lastname: Jin
authors.forename: Bing
authors.initials: B
authors.lastname: Niu
authors.forename: Kai-Yan
authors.initials: KY
authors.lastname: Feng
authors.forename: Wen-Cong
authors.initials: WC
authors.lastname: Lu
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
authors.forename: Guo-Zheng
authors.initials: GZ
authors.lastname: Li
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D030562:Databases, Protein; D011506:Proteins / Q000032:analysis* / Q000737:chemistry / Q000378:metabolism; D012984:Software; D013347:Subcellular Fractions / Q000737:chemistry / Q000378:metabolism
chemical_list: D011506:Proteins