Enhanced prediction of anti-tubercular peptides from sequence information using divergence measure-based intuitionistic
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METHODOLOGIES AND APPLICATION
Enhanced prediction of anti-tubercular peptides from sequence information using divergence measure-based intuitionistic fuzzyrough feature selection Pankhuri Jain1 • Anoop Kumar Tiwari2 • Tanmoy Som1
Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keywords Dependency function Feature extraction Feature selection Intuitionistic fuzzy divergence measure Intuitionistic fuzzy set
1 Introduction
Communicated by V. Loia. & Anoop Kumar Tiwari [email protected] Pankhuri Jain [email protected] Tanmoy Som [email protected] 1
Department of Mathematical Sciences, Indian Institute of Technology, Banaras Hindu University, Varanasi 221005, India
2
Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi 221005, India
Tuberculosis, caused by Mycobacterium tuberculosis (M. tuberculosis), is a worldwide health malady that claims almost 1.8 million lives annually. In ‘WHO Global Tuberculosis Report-2017,’ it was declared that Tuberculosis (TB) is one of the top ten leading causes of death worldwide (W
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