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Table 1 Comparison of Random Forest and Support Vector Machine classifiers for predicting tissue-specific genes.
From: Genome-wide prediction and analysis of human tissue-selective genes using microarray expression data
| Tissue | Method |
AC (%) |
SN (%) |
SP (%) | MCC |
ROC AUC |
|---|---|---|---|---|---|---|
| Brain | SVM |
92.07 (± 0.302) |
54.23 (± 1.227) | 95.82 (± 0.263) | 0.5091 (± 0.015) |
0.8937 (± 0.003) |
| RF |
93.48 (± 0.240) |
53.73 (± 1.485) | 97.43 (± 0.153) | 0.5676 (± 0.016) | 0.9488 (± 0.002) | |
| Liver | SVM |
97.29 (± 0.421) |
84.11 (± 2.281) | 98.61 (± 0.309) | 0.8350 (± 0.025) | 0.9854 (± 0.004) |
| RF |
97.29 (± 0.341) |
79.00 (± 1.355) | 99.12 (± 0.255) | 0.8290 (± 0.0213) | 0.9777 (± 0.002) |
