Abstract
Successful prediction of bacterial toxins directly from primary sequence is much benefited to further basic knowledge of cell biology or for medical research and application. In this paper, we proposed a new method to predict bacterial toxins by using the feature representation of position specific scoring matrix and IB1 classifier fusion. The jackknife cross-validation is applied to test predictive capability of the proposed method. The predictive results showed that the total prediction accuracy is 96.62% for bacterial toxins and non toxins, which is higher than previous methods. Furthermore, we also discriminated endotoxin and exotoxin by the proposed method, and obtained satisfactory result with a total prediction accuracy 95.33%.
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Song, C. (2011). Prediction of Bacterial Toxins by Feature Representation of Position Specific Scoring Matrix and IB1 Classifier Fusion. In: Qi, L. (eds) Information and Automation. ISIA 2010. Communications in Computer and Information Science, vol 86. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19853-3_95
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DOI: https://doi.org/10.1007/978-3-642-19853-3_95
Publisher Name: Springer, Berlin, Heidelberg
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