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Table 5 Available methods. The results column contains the metric and what the classifier is predicting.

From: HIV-1 coreceptor usage prediction without multiple alignments: an application of string kernels

Reference Learning method Training set Testing set Multiple alignments Results
Pillai et al. 2003 Charge rule [5, 6] 271 - yes Accuracy (CXCR4): 87.45%
Resch et al. 2001 Neural networks 181 - yes Specificity (X4): 90.00%
Pillai et al. 2003 SVM 271 - yes Accuracy (CXCR4): 90.86%
Jensen et al. 2003 PSSM1 213 175 yes Specificity (CXCR4): 96.00%
Jensen et al. 2006 PSSM 279 - yes Specificity (CXCR4): 94.00%2
Sander et al. 2007 SVM 432 - yes Accuracy (CXCR4): 91.56%
Xu et al. 2007 Random forests 651 - yes Accuracy (R5): 95.10%
Lamers et al. 2008 Neural networks 149 - yes Accuracy (R5X4): 75.50%
This manuscript SVM 1425 1425 no Accuracy (CXCR4): 94.80%
  1. 1Position-specific scoring matrices
  2. 2Subtype C