Table 4 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 | 87.1% (2.8%) | 91.9% (1.6%) | 89.0% (1.2%) | 89.1% (1.6%) | 87.2% (2.2%) | 87.8% (0.7%) |
Colorectal | 87.8% (1.7%) | 94.0% (1.6%) | 90.6% (0.8%) | 86.1% (3.8%) | 90.8% (2.9%) | 87.5% (2.2%) |
Uterus | 90.0% (1.8%) | 72.8% (6.8%) | 78.0% (5.5%) | 84.9% (3.1%) | 71.4% (2.8%) | 76.6% (1.0%) |
Brain | 88.3% (4.4%) | 58.6% (5.2%) | 67.9% (2.2%) | 77.3% (2.5%) | 75.9% (3.5%) | 75.6% (1.5%) |
Lung | 69.1% (4.9%) | 79.9% (7.2%) | 69.8% (2.7%) | 73.7% (3.1%) | 75.6% (4.0%) | 73.4% (1.8%) |
Kidney | 81.9% (2.7%) | 81.3% (2.0%) | 81.3% (1.5%) | 70.0% (3.8%) | 76.0% (5.0%) | 71.2% (3.3%) |
Prostate | 76.4% (6.1%) | 67.3% (7.2%) | 66.0% (3.7%) | 72.2% (2.7%) | 65.5% (3.5%) | 68.1% (2.3%) |