Table 3 Machine learning experiment test results of gene expression data
From: A novel gene selection method for gene expression data for the task of cancer type classification
Feature Sel. | Feature count | Accuracy | F-Score | Roc-Auc | FPR | MCC |
|---|---|---|---|---|---|---|
FPKM all features | 60483 | 99.46 ±0.42 | 99.46 ±0.42 | 99.45 ±0.50 | 0.40 ±0.31 | 99.09 ±0.71 |
SelectKBest | 10000 | 99.57 ±0.13 | 99.57 ±0.13 | 99.61 ±0.18 | 0.29 ±0.13 | 99.27 ±0.22 |
SelectKBest | 5000 | 99.30 ±0.27 | 99.30 ±0.27 | 99.47 ±0.22 | 0.46 ±0.24 | 98.82 ±0.46 |
SelectKBest | 1000 | 98.92 ±0.38 | 98.92 ±0.38 | 99.13 ±0.41 | 0.71 ±0.32 | 98.18 ±0.64 |
mRMR | 5000 | 98.98 ±0.43 | 98.98 ±0.43 | 99.08 ±0.33 | 0.73 ±0.23 | 98.28 ±0.72 |
mRMR | 1000 | 98.44 ±0.52 | 98.45 ±0.52 | 98.63 ±0.56 | 0.82 ±0.32 | 97.37 ±0.88 |
Relief | 10000 | 98.44 ±0.31 | 98.45 ±0.31 | 98.51 ±0.45 | 0.82 ±0.28 | 97.37 ±0.53 |
Relief | 5000 | 99.30 ±0.36 | 99.30 ±0.36 | 99.34 ±0.37 | 0.27 ±0.14 | 99.34 ±0.37 |
Relief | 1000 | 99.46 ±0.17 | 99.46 ±0.17 | 99.54 ±0.16 | 0.39 ±0.14 | 99.09 ±0.29 |
Hallmark | 4266 | 99.57 ±0.22 | 99.57 ±0.21 | 99.57 ±0.23 | 0.29 ±0.14 | 99.27 ±0.36 |
VCF 3000 Effective Genes | 6752 | 99.57 ±0.47 | 99.57 ±0.47 | 99.64 ±0.37 | 0.31 ±0.34 | 99.27 ±0.79 |
VCF 3500 Effective Genes | 7741 | 99.68 ±0.40 | 99.68 ±0.39 | 99.72 ±0.30 | 0.24 ±0.26 | 99.46 ±0.67 |