Table 3. Results of mRMR feature selection for an SVM for predicting outcome of paclitaxel therapy.
| Data | CT 1 | HT | CT+HT | ||||||
|---|---|---|---|---|---|---|---|---|---|
|
Survival years
(as threshold) |
3 | 4 | 5 | 3 | 4 | 5 | 3 | 4 | 5 |
| # patients 2 | 53 | 420 | 504 | ||||||
|
Accuracy (TP)
(%) |
81.1 | 81.1 | 84.9 | 85.7 | 79.5 | 72.9 | 83.1 | 74.8 | 67.9 |
| Precision | 0.809 | 0.813 | 0.852 | 0.878 | 0.765 | 0.692 | 0.795 | 0.703 | 0.662 |
| F-Measure | 0.809 | 0.811 | 0.845 | 0.794 | 0.726 | 0.663 | 0.772 | 0.672 | 0.666 |
| MCC | 0.582 | 0.625 | 0.675 | 0.119 | 0.17 | 0.173 | 0.161 | 0.137 | 0.238 |
| AUC | 0.783 | 0.812 | 0.82 | 0.508 | 0.533 | 0.548 | 0.53 | 0.531 | 0.61 |
|
SVM Par.
(gamma) |
0.0 | 0.5 | 1.0 | 1.0 | 0.75 | 1.5 | 0.75 | 0.5 | 1.0 |
|
SVM Par.
(cost) |
64 | 128 | 8 | 2 | 64 | 2 | 16 | 2 | 2 |
|
Selected
genes |
MAP4,
GBP1, FN1, MAPT, BBC3, FGF2, NFKB2, TUBB4B |
TWIST1,
FN1, BBC3, FGF2, BCL2L1 |
ABCB11,
BCL2, GBP1, SLCO1B3, ABCB1, BAD, TUBB4A, MAPT, NFKB2, TUBB4B |
ABCB11,
BCL2, MAP4, TUBB1, GBP1, SLCO1B3, ABCB1, BAD, TWIST1, FN1, TUBB4A, MAPT, OPRK1, BBC3, FGF2, NFKB2, ABCC1, NR1I2 |
BAD,
GBP1, MAPT, BBC3 |
ABCB11,
MAP4, SLCO1B3, BAD, FN1, OPRK1, BBC3, NFKB2, NR1I2, TUBB4B |
ABCB11,
SLCO1B3, BAD, TUBB4A, MAPT, BBC3, FGF2, NFKB2, ABCC1, NR1I2 |
ABCB11,
BMF, BCL2, MAP4, TUBB1, GBP1, SLCO1B3, ABCB1, BAD, TWIST1, FN1, MAPT, OPRK1, BBC3, FGF2, NFKB2, ABCC1, NR1I2, TUBB4B |
MAP4,
GBP1, SLCO1B3, BAD, MAPT, OPRK1, BBC3, NFKB2, ABCC1, NR1I2, TUBB4B |
1For patients treated with CT with ≥4 Yr survival and CT+ HT for ≥ 5 Yr, the cost for the mRMR model was set to 64. Of those treated with CT for ≥ 4 Yr, genes were selected using a greedy, stepwise forward search, while in other cases, greedy stepwise backward search was used. Also, gamma = 0 in all cases. 2Predicted responses for individual METABRIC patients are provided in Dataset 1.