Abstract
Using Relevance Vector Machine, and with an improved feature extraction method, a novel method was proposed here to predict bacterial toxins, the jackknife cross-validation was applied to test the predictive capability of the proposed method. Our method achieved a total accuracy of 95.95% for bacterial toxin and non-toxin, and a total accuracy of 97.33% for discriminating endotoxins and exotoxins, the satisfactory results showed that our method is effective and could play a complementary role to bacterial toxins prediction.
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References
Böhnel, H., Gessler, F.: Botulinum Toxins – Cause Of Botulism and Systemic Diseases? Vet. Res. Commun. 29, 313–345 (2005)
Kessler, K.R., Benecke, R.: Botulinum Toxin: From Poison to Remedy. Neurotoxicology 18, 761–770 (1997)
Carter, S.R., Seiff, S.R.: Cosmetic Botulinum Toxin Injections. Int. Ophthalmol. Clin. 37, 69–79 (1997)
Saha, S., Raghava, G.P.: BTXpred: Prediction of Bacterial Toxins. In Silico Biol. 7, 405–412 (2007)
Yang, L., Li, Q.Z., Zuo, Y.C., Li, T.: Prediction of Animal Toxins Using Amino Acid Composition and Support Vector Machine. Inner Mongolia University 40, 443–448 (2009)
Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M.C., Estreicher, A., Gasteiger, E., Martin, M.J., Michoud, K., O’Donovan, C., Phan, I., Pilbout, S., Schneider, M.: The Swiss-Prot Protein Knowledgebase and Its Supplement TrEMBL in 2003. Nucleic Acids Res. 31, 365–370 (2003)
Wei, Z.L., Godzik, A.: Cd-Hit: A Fast Program for Clustering and Comparing Large Sets Of Protein or Nucleotide Sequences. Bioinformatics 22, 1658–1659 (2006)
Sci., USA, vol. 88, pp. 2297–2301 (1991)
Richman, J.S., Moorman, J.R.: Physiological Time-Series Analysis Using Approximate Entropy and Sample Entropy. Am. J. Physiol. Heart Circ. Physiol. 278, 2039–2049 (2001)
Tipping, M.E.: Sparse Bayesian Learning and the Relevance Vector Machine. JMLR 1, 211–244 (2001)
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Song, C. (2011). Prediction of Bacterial Toxins by Relevance Vector Machine. In: Yu, Y., Yu, Z., Zhao, J. (eds) Computer Science for Environmental Engineering and EcoInformatics. CSEEE 2011. Communications in Computer and Information Science, vol 159. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22691-5_47
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DOI: https://doi.org/10.1007/978-3-642-22691-5_47
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