Research Article
Prediction and Analysis of Retinoblastoma Related Genes through Gene Ontology and KEGG
Table 1
The predicted results for ten datasets.
| Dataset | Optimal feature number | Sn | Sp | Acc | MCC |
| 1 | 156 | 0.5727 | 0.9291 | 0.8697 | 0.5174 | 2 | 141 | 0.6273 | 0.9218 | 0.8727 | 0.5452 | 3 | 337 | 0.7364 | 0.8691 | 0.8470 | 0.5347 | 4 | 140 | 0.6000 | 0.9327 | 0.8773 | 0.5471 | 5 | 126 | 0.5636 | 0.9436 | 0.8803 | 0.5434 | 6 | 489 | 0.6273 | 0.9255 | 0.8758 | 0.5527 | 7 | 78 | 0.5545 | 0.9527 | 0.8864 | 0.5588 | 8 | 222 | 0.6364 | 0.9345 | 0.8848 | 0.5795 | 9 | 319 | 0.6545 | 0.9218 | 0.8773 | 0.5663 | 10 | 235 | 0.5545 | 0.9491 | 0.8833 | 0.5495 |
| Mean (standard deviation) | 0.6127 (0.0567) | 0.928 (0.0234) | 0.8755 (0.0113) | 0.5494 (0.017) |
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Sn: sensitivity; Sp: specificity; Acc: accuracy; MCC: Matthews’s correlation coefficient.
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