African Journal of
Microbiology Research

  • Abbreviation: Afr. J. Microbiol. Res.
  • Language: English
  • ISSN: 1996-0808
  • DOI: 10.5897/AJMR
  • Start Year: 2007
  • Published Articles: 5233

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.
Email: [email protected]

  •  Accepted: 10 March 2011
  •  Published: 18 June 2011

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.