Enhancing the Prediction of Anti-cancer Peptides by Suitable Feature Extraction and FRFS with ACO Search Followed by Resampling

Abstract

In this paper, we have presented an effective methodology to improve the prediction performances of learning algorithms to forecast the anti-cancer peptides. Firstly, 489 informative features are extracted based on 11 interesting compositions. Thereafter, 117 non-redundant and relevant features are selected by using fuzzy rough feature selection (FRFS) with ant colony optimization (ACO) search. Then, instances of the reduced dataset are resampled by using synthetic minority optimization technique (SMOTE) to achieve the optimally balancing ratio, i.e. 1:1. Next, we conduct comprehensive experiments with reduced and unreduced datasets by using various learning algorithms based on tenfold cross validation (CV) and percentage split 80:20 validation. Finally, we represent a comparative study of our proposed methodology with possible alternatives and prove that it outperforms the previous existing methods. Experimental results indicate that the best results are produced by vote-based classifier using percentage split of 80:20 validation technique with specificity of 99.1%, sensitivity of 97.3%, accuracy of 98.2%, AUC of 0.983, and MCC of 0.888. From the experimentation, it can be concluded that our current methodology can enhance the discriminating ability of different artificial intelligence models for anti-cancer and non-anti-cancer peptides by using feature extraction, FRFS with ACO followed by SMOTE, and vote-based classifier. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.