Solihah Binti Primary Author mixed material bibliography Purwokerto Universitas Muhammadiyah Purwokerto 2021 e English

Artikel Jurnal

hlm : 131-138 Abstract A conformational epitope is a part of a protein-based vaccine. It is challenging to identify using an experiment. A computational model is developed to support identification. However, the imbalance class is one of the constraints to achieving optimal performance on the conformational epitope B cell prediction. In this paper, we compare several conformational epitope B cell prediction models from non-ensemble and ensemble approaches. A sampling method from Random undersampling, SMOTE, and cluster-based undersampling is combined with a decision tree or SVM to build a non-ensemble model. A random forest model and several variants of the bagging method is used to construct the ensemble model. A 10-fold cross-validation method is used to validate the model. The experiment results show that the combination of the cluster-based under-sampling and decision tree outperformed the other sampling method when combined with the non-ensemble and the ensemble method. This study provides a baseline to improve existing models for dealing with the class imbalance in the conformational epitope prediction. Informatika JUITA 20869398 Perpustakaan Teknik UPI YAI JUITA V9N1 Mei 2021 JUITA5a-016 Perpustakaan FT UPI YAI JUITA V9N1 Mei 2021 JUITA5b-016 Perpustakaan FT UPI YAI JUITA V9N1 Mei 2021 JUITA5c-016 Perpustakaan FT UPI YAI JUITA V9N1 Mei 2021 juita.png.png 58439 2022-12-08 16:10:19 2023-01-12 14:28:27 machine generated