| Su ECY |
------>authors3_c= ------>paper_class1=2 ------>Impact_Factor=None ------>paper_class3=0 ------>paper_class2=0 ------>vol= ------>confirm_bywho=None ------>insert_bywho=emilysu ------>Jurnal_Rank=None ------>authors4_c= ------>comm_author= ------>patent_EDate=None ------>authors5_c= ------>publish_day=1 ------>paper_class2Letter=None ------>page2= ------>medlineContent= ------>unit=000 ------>insert_date=20091012 ------>iam=1 ------>update_date=None ------>author=??? ------>change_event=1 ------>ISSN= ------>authors_c= ------>score=500 ------>journal_name=Proceedings of IEEE Computational Systems Bioinformatics Conference ------>paper_name=Protein subcellular localization prediction based on compartment-specific biological features ------>confirm_date=None ------>tch_id=097199 ------>pmid=17369650 ------>page1= ------>fullAbstract=Prediction of subcellular localization of proteins is important for genome annotation, protein function prediction, and drug discovery. We present a prediction method for Gram-negative bacteria that uses ten one-versus-one support vector machine (SVM) classifiers, where compartment-specific biological features are selected as input to each SVM classifier. The final prediction of localization sites is determined by integrating the results from ten binary classifiers using a combination of majority votes and a probabilistic method. The overall accuracy reaches 91.4%, which is 1.6% better than the state-of-the-art system, in a ten-fold cross-validation evaluation on a benchmark data set. We demonstrate that feature selection guided by biological knowledge and insights in one-versus-one SVM classifiers can lead to a significant improvement in the prediction performance. Our model is also used to produce highly accurate prediction of 92.8% overall accuracy for proteins of dual localizations. ------>tmu_sno=None ------>sno=22476 ------>authors2=Lo A ------>authors3=Chiu HS ------>authors4=Sung TY ------>authors5=Hsu WL ------>authors6= ------>authors6_c= ------>authors=Su ECY ------>delete_flag=0 ------>SCI_JNo=None ------>authors2_c= ------>publish_area=2 ------>updateTitle=Protein subcellular localization prediction based on compartment-specific biological features. ------>language=2 ------>check_flag=None ------>submit_date=None ------>country=NULL ------>no= ------>patent_SDate=None ------>update_bywho=None ------>publish_year=2006 ------>submit_flag=None ------>publish_month=8 |