Table 2. Performance of second-generation siRNA efficacy prediction algorithms on T737, V185, and V419.
| Pearson Correlation Coefficient (PCC) | |||||||||
|---|---|---|---|---|---|---|---|---|---|
| S. No. | Reference | Technique | siRNA Dataset | ASP-siRNA Dataset | Train# | Val# | T737 | V185 | V419* |
| 1 | Huesken et al. (2005) | ANN | Huesken2431 | ✗ | 0.67 | 0.66 | Webserver not working | 0.54 | |
| 2 | Vert et al. (2006) | LR | Huesken2431 | ✗ | 0.67 | 0.57 | Webserver not working | 0.55 | |
| 3 | Jiang et al. (2007) | RFR | 3589 | ✗ | 0.85 | 0.59 | Webserver not working | NA | |
| 4 | Ichihara et al. (2007) | LR | Huesken2431 | ✗ | 0.72 | NA | 0.18 | 0.14 | 0.56 |
| 5 | Ahmed and Raghava (2011) | SVM | Huesken2431 | ✗ | 0.65 | 0.65 | 0.27 | 0.25 | 0.55 |
| 6 | siRNApred Kumar et al., (2009) | SVM | Huesken2431 | ✗ | 0.56 | 0.47 | 0.27 | 0.09 | 0.23 |
Second-generation siRNA efficacy algorithms were developed on the Huesken dataset. S.No., Serial number; RFR, random forest regression; ANN, artificial neural network; LR, linear regression; Train# and Val# is the performance during n-fold cross-validation and independent validation of a particular algorithm. T737 and V185 column reflects the performance of algorithms on training/testing and independent validation sets of ASPsiPredSVM (in bold italics), while extreme right column indicates performance of algorithms on benchmarking dataset V419.