Table 5 Prediction rule for two classifiers based on one replicate
From: Large-scale risk prediction applied to Genetic Analysis Workshop 17 mini-exome sequence data
Feature | Empirical Bayes classifier | Random forest classifier | ||||
|---|---|---|---|---|---|---|
Genes | #SNP | MAF | Genes | #SNP | MAF | |
1 | Age | Age | ||||
2 | Smoke | Smoke | ||||
3 | GOLGA1 | 1 | <0.01 | OR1L6 | <0.01 | |
1 | 0.01–0.05 | 3 | 0.01–0.05 | |||
1 | ≥0.05 | 1 | ≥0.05 | |||
4 | FLT1 | 25 | <0.01 | VTI1B | 9 | <0.01 |
7 | 0.01–0.05 | 1 | 0.01–0.05 | |||
3 | ≥0.05 | 1 | ≥0.05 | |||
5 | NFKBIA | 6 | <0.01 | DENND1A | 19 | <0.01 |
0.01–0.05 | 3 | 0.01–0.05 | ||||
2 | ≥0.05 | 4 | ≥0.05 | |||
6 | DGKZ | 17 | <0.01 | C9ORF66 | 4 | <0.01 |
4 | 0.01–0.05 | 3 | 0.01–0.05 | |||
1 | ≥0.05 | 4 | ≥0.05 | |||
7 | SMTN | 23 | <0.01 | CECR1 | 8 | <0.01 |
4 | 0.01–0.05 | 0.01–0.05 | ||||
2 | ≥0.05 | 4 | ≥0.05 | |||
8 | PAK7 | 1 | 0.30 | MAP3K12 | 14 | <0.01 |
3 | 0.01–0.05 | |||||
≥0.05 | ||||||
9 | ADAM15 | 22 | <0.01 | SLC20A2 | 24 | <0.01 |
5 | 0.01–0.05 | 4 | 0.01–0.05 | |||
3 | ≥0.05 | 1 | ≥0.05 | |||
10 | ADAMTS4 | 33 | <0.01 | ALK | 9 | <0.01 |
4 | 0.01–0.05 | 1 | 0.01–0.05 | |||
3 | ≥0.05 | 6 | ≥0.05 | |||