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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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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