Support Vector Machine-based method for predicting subcellular localization of mycobacterial proteins using evolutionary information and motifs.

ABSTRACT:

Background

In past number of methods have been developed for predicting subcellular location of eukaryotic, prokaryotic (Gram-negative and Gram-positive bacteria) and human proteins but no method has been developed for mycobacterial proteins which may represent repertoire of potent immunogens of this dreaded pathogen. In this study, attempt has been made to develop method for predicting subcellular location of mycobacterial proteins.

Results

The models were trained and tested on 852 mycobacterial proteins and evaluated using five-fold cross-validation technique. First SVM (Support Vector Machine) model was developed using amino acid composition and overall accuracy of 82.51% was achieved with average accuracy (mean of class-wise accuracy) of 68.47%. In order to utilize evolu

SUBMITTER: Rashid M 

PROVIDER: S-EPMC2147037 | biostudies-literature | 2007 Sep

REPOSITORIES: biostudies-literature