PIP-EL: A New Ensemble Learning Method for Improved Proinflammatory Peptide Predictions - Manavalan, Balachandran; Shin, Tae Hwan; Kim, Myeong Ok; Lee, Gwang (Frontiers in Immunology, 2018)
Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathogens. Proinflammatory inducing peptides (PIPs) have been used as an antineoplastic agent, an antibacterial agent and a vaccine in immunization therapies. Identification of PIPs using classical wet-lab methods is expensive and time consuming. Therefore, efficient computational methods are needed to identify the possible candidates before experimentation. In this study, we proposed a new predictor, PIP-EL, for predicting PIPs using the strategy of ensemble learning (EL). Our benchmarking dataset is imbalanced. Thus, we applied a random under-sampling technique to generate 10 balanced models for each composition. Technically, PIP-EL is the fusion of 50 independent random forest models, where each of the five different compositions, including amino acid, dipeptide, composition-transition-distribution, physicochemical properties and amino acid index contains 10 RF models. PIP-EL achieves the Matthews’ correlation coefficient (MCC) of 0.435 in a 5-fold cross-validation test, which is ~2–5 % higher than that of the individual classifiers and hybrid feature-based classifier. Furthermore, we evaluate the performance of PIP-EL on the independent dataset, showing that our method outperforms the existing method and two different machine learning methods developed in this study, with an MCC of 0.454. These results indicate that PIP-EL will be a useful tool for predicting PIPs and for researchers working in the field of peptide therapeutics and immunotherapy. The user-friendly web server, PIP-EL, is freely accessible.