Table 2 Precision and recall for our proposed model
From: Deep learning for cancer type classification and driver gene identification
| Â | Germline sequence | Cancer sequence | ||||
|---|---|---|---|---|---|---|
Precision | Recall | F-measure | Precision | Recall | F-measure | |
Breast | 81.9% (2.7%) | 83.4% (4.2%) | 81.4% (1.8%) | 85.6% (2.0%) | 90.7% (1.8%) | 87.8% (1.1%) |
Colorectal | 85.9% (1.9%) | 83.9% (2.1%) | 84.6% (0.8%) | 84.7% (4.5%) | 87.1% (2.6%) | 84.7% (2.1%) |
Brain | 73.0% (1.5%) | 66.5% (4.3%) | 68.5% (1.9%) | 87.5% (2.7%) | 78.4% (2.0%) | 82.2% (0.9%) |
Uterus | 76.3% (4.9%) | 62.7% (6.9%) | 64.2% (3.9%) | 85.3% (1.8%) | 68.2% (3.3%) | 75.1% (1.7%) |
Lung | 64.8% (3.2%) | 75.5% (4.7%) | 67.7% (1.5%) | 70.3% (4.9%) | 78.4% (5.5%) | 71.2% (2.0%) |
Kidney | 76.9% (2.5%) | 71.5% (2.8%) | 73.4% (1.1%) | 77.6% (4.7%) | 68.7% (5.9%) | 69.5% (2.3%) |
Prostate | 70.8% (4.2%) | 55.7% (6.0%) | 58.9% (3.2%) | 65.6% (5.5%) | 50.5% (9.7%) | 49.1% (5.9%) |