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Journal of Virology, April 2006, p. 4205, Vol. 80, No. 8
0022-538X/06/$08.00+0 doi:10.1128/JVI.80.8.4205.2006
| AUTHOR'S CORRECTION |
School of Information Science, Computer and Electrical Engineering, Halmstad University, Halmstad, Sweden, and Division of Hematology and Transfusion Medicine, Department of Laboratory Medicine, Lund University, Lund, Sweden, and Division of Molecular Toxicology, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
Volume 79, number 19, p. 12477-12486, 2005. Page 12480: Figure 3 and its legend should appear as shown below. The out-of sample prediction performance for the Gaussian support vector machine (GSVM) algorithm was overestimated due to a computational mistake. As a result, the GSVM algorithm with hydrophobicity and size coding does not outperform the linear algorithms with sparse orthogonal coding. However, the two physicochemical parameters hydrophobicity and size are still the best pair of properties for predicting cleavage by the HIV-1 protease. As previously stated, there is no statistically significant difference (at the 95% level) in prediction performance between the best method using sparse orthogonal coding and the GSVM model with property coding. None of the other results or conclusions in the original paper are affected by the computational mistake.
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FIG. 3. The best predictors out-of-sample performances, estimated using cross-validation. There is no statistically significant difference (at the 95% level) between the best linear and the best nonlinear predictors. The two bottom curves are both for property-coded data, but the upper one represents the case when care is not taken to avoid sequence bias in the testing (shown here to illustrate the importance of avoiding such bias and overly optimistic results). Here S denotes small property and H denotes hydrophobicity.
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