JPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning Method.

ABSTRACT: Different types of J-proteins perform distinct functions in chaperone processes and diseases development. Accurate identification of types of J-proteins will provide significant clues to reveal the mechanism of J-proteins and contribute to developing drugs for diseases. In this study, an ensemble predictor called JPPRED for J-protein prediction is proposed with hybrid features, including split amino acid composition (SAAC), pseudo amino acid composition (PseAAC), and position specific scoring matrix (PSSM). To deal with the imbalanced benchmark dataset, the synthetic minority oversampling technique (SMOTE) and undersampling technique are applied. The average sensitivity of JPPRED based on above-mentioned individual feature spaces lies in the range of 0.744-0.851, indicating the discriminat

SUBMITTER: Zhang L 

PROVIDER: S-EPMC4637456 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature