Table 5 |
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|
Generic feature selection (gene-level) |
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|
# Method |
# Selected Genes |
Accuracy |
Avg Subrank |
|||
|
Median |
Avg |
σ |
Iqr |
|||
|
|
||||||
|
IG |
22 |
90.2 |
81.5 |
18.1 |
30.7 |
15.0 |
|
IG |
228 |
89.8 |
82.0 |
17.9 |
30.3 |
14.5 |
|
SVM-RFE |
228 |
88.3 |
82.3 |
16.7 |
28.5 |
16.4 |
|
SVM-RFE |
22 |
88.0 |
82.1 |
17.2 |
30.4 |
16.2 |
|
|
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|
Performance of the baseline classification method equipped with a feature-selection step prior to learning. Features (genes) are ranked by the information gain and SVM-RFE heuristics. The number of selected top-ranking genes (22 and 228, respectively) corresponds to the mean number of unique genes acting in gene sets selected in the 1 and 1:10 (respectively) alternatives of the set-level workflow. |
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|
Holec et al. BMC Bioinformatics 2012 13(Suppl 10):S15 doi:10.1186/1471-2105-13-S10-S15 |
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