ArticleBioMed research international2015
JPPRED: Prediction of Types of J-Proteins from Imbalanced Data Using an Ensemble Learning Method.
Article in BioMed research international, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Identifying Heat Shock Protein Families from Imbalanced Data by Using Combined Features.Computational and mathematical methods in medicine · 2020Article
- ir-HSP: Improved Recognition of Heat Shock Proteins, Their Families and Sub-types Based OnFrontiers in genetics · 2017Article
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4 authors.
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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 discriminative power of these features. In addition, JPPRED yields the highest average sensitivity of 0.875 using the hybrid feature spaces of SAAC, PseAAC, and PSSM. Compared to individual base classifiers, JPPRED obtains more balanced and better performance for each type of J-proteins. To evaluate the prediction performance objectively, JPPRED is compared with previous study. Encouragingly, JPPRED obtains balanced performance for each type of J-proteins, which is significantly superior to that of the existing method. It is anticipated that JPPRED can be a potential candidate for J-protein prediction.
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