Enhanced prediction of anti-tubercular peptides from sequence information using divergence measure-based intuitionistic fuzzy-rough feature selection

Abstract

Tuberculosis is one of the leading causes of millions of deaths across the world, mainly due to growth of drug-resistant strains. Anti-tubercular peptides may facilitate an alternate way to combat antibiotic tolerance. This study describes a novel approach for enhancing the prediction of anti-tubercular peptides by feature extraction from sequence of the peptides, selection of optimal features from the extracted features, and selection of suitable learning algorithm. Firstly, we extract different sequence features by using iFeature web server. Then, the optimal features are obtained by using a novel divergence measure-based intuitionistic fuzzy rough sets-assisted feature selection technique. Furthermore, an attempt has been made to develop models using different machine learning techniques for enhancing the prediction of anti-tubercular (or anti-mycobacterial peptides) with other antibacterial peptides (ABP) as well non-antibacterial peptides (non-ABP). Moreover, the best prediction result is obtained by vote-based classifier. Using 80:20 percentage split, the proposed method performs well, with sensitivity of 92.0%, 96.4%, specificity of 83.3%, 88.4%, overall accuracy of 87.80%, 92.90%, Mathews correlation coefficient of 0.757, 0.857, AUC of 0.922, 0.914, and g-means of 87.5%, 92.3% for anti-tubercular and ABP (primary dataset), anti-tubercular and non-ABP (secondary dataset), respectively. Finally, we have evaluated the performances of different machine learning algorithms by using the reduced training sets as produced by our proposed feature selection technique as well as already existing intuitionistic fuzzy rough set based and ensemble feature selection technique. Moreover, the performance of our proposed approach is evaluated on few benchmark and AMP datasets. From the experimental results, it can be observed that our proposed method is outperforming the previous methods.