Table 4 Performance statistics of selected genes on MS data
Training set (10-fold CV results) | Test set | |||||||
|---|---|---|---|---|---|---|---|---|
A. Performance comparison | ||||||||
Method (n) | Error (%) | GBS | BCM | AUPR | Error (%) | GBS | BCM | AUPR |
SAMGSR (52) | 34.09 | 0.244 | 0.570 | 0.645 | 46.67 | 0.465 | 0.501 | 0.725 |
W-SAMGSR (25) | 31.82 | 0.191 | 0.611 | 0.771 | 43.33 | 0.341 | 0.564 | 0.860 |
LASSO (30) | 34.09 | 0.275 | 0.632 | 0.672 | 46.67 | 0.377 | 0.499 | 0.747 |
Penalized SVM(11) | 47.73 | 0.406 | 0.534 | 0.630 | 45 | 0.569 | 0.431 | 0.555 |
gelnet (169) | 34.09 | 0.251 | 0.528 | 0.589 | 46.67 | 0.246 | 0.547 | 0.746 |
RRFE (198) | 43.18 | 0.263 | 0.547 | 0.619 | 46.67 | 0.300 | 0.523 | 0.693 |
B. Performance of the top 3 teams in sbv MS sub-challenge (among 54 teams) | ||||||||
Study (size) | Training data used/Method used | Error (%) | GBS | BCM | AUPR | |||
Lauria’s (n > 100) | E-MTAB-69/Mann-Whitney test, then use top α % of the selected genes and Cytoscape to get the clusters on the test set | -- | -- | 0.884 | 0.874 | |||
Tarca’s (n = 2) | GSE21942 (on Human Gene 1.0 ST)/LDA | -- | -- | 0.629 | 0.819 | |||
Zhao’s (n = 58) | 7 other data and E-MTAB-69/Elastic net | 30 | -- | 0.576 | 0.820 | |||