A multi class random forest (MCRF) model for classification of small plant peptides
Ankita Tripathi, Tapas Goswami, Shrawan Kumar Trivedi, Ravi Datta Sharma
Abstract
Research on the classification of the different categories of small peptides is becoming a challenge for bioinformatics domain. However, machine learning models have shown their potential to tackle such applications. We propose a multi-class random forest (MCRF) classifier to classify small peptides which is compared with state-of-art classifiers including, support vector machine with RBF kernel (SVM+RBF), naïve Bayes (NB), Decision Tree (C5.0), Random Forest (RF). Small peptides sequences are selected from ARA-PEPs repository (Hazarika, et al., 2017) where 13748 small peptides are listed with six categories (i.e., secreted, sORF, stress-induced peptides (SIP), secreted-sORF, sORF-SIP, SIP-secreted). Total 27 features are fetched for each small peptides sequence to prepare data. Comparison is done using metrics i.e., F-Value, Sensitivity, Specificity, ROC, and FP rate with some statistical validation i.e., Kappa Statistics and Wilcoxon sign ranked test. Results of this study show that the proposed classifier has potential to accurately classify multi-level imbalanced data.