ISSN 2736-1756
Advanced Journal of Microbiology Research ISSN 2241-9837 Vol. 13 (2), pp. 001-005, February, 2019. © International Scholars Journals
Full Length Research Paper
Prediction of bacterial toxins by an improved feature extraction and IB1 algorithm fusion
Chaohong Song
College of Science, Huazhong Agricultural University, Wuhan 430070, China. E-mail: [email protected]. Tel: +86 189 71212816. Fax: +86 027 87282133.
Accepted 10 January, 2019
Abstract
Correctly identifying bacterial toxin is of great benefit to cell biology and medical research. In order to improve predictive accuracy, based on the concept of pseudo amino acid composition, combined with the methods of approximate entropy and IB1 algorithm, a new method is proposed to predict bacterial toxins in this paper. The improved method gives comprehensive consideration of amino acid composition, side-chain mass of the amino acid, hydrophilic, and hydrophobic characteristics of a protein sequence. The total prediction accuracy of our method was 97.52% for bacterial toxin and non-toxin, and 97.33% for discriminating endotoxins from exotoxins, which were much higher than that of the previous methods.
Key words: Approximate entropy, IB1 algorithm, bacterial toxin, exotoxins, endotoxins, pseudo amino acid composition.