Table 1 |
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Comparison between NN and SVM (LibSVM and SMO) |
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|
Dataset |
NN |
LibSVM |
SMO |
|
|
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|
S1_NPH_CH2 |
318 |
158 |
174 |
|
S1_NPH_CH3 |
398 |
138 |
95 |
|
S1_PH_CH2 |
132 |
88 |
87 |
|
S1_PH_CH3 |
210 |
48 |
111 |
|
S2_NPH_CH2 |
72 |
37 |
50 |
|
S2_NPH_CH3 |
88 |
34 |
40 |
|
S2_PH_CH2 |
176 |
71 |
120 |
|
S2_PH_CH3 |
236 |
154 |
139 |
|
S3_NPH_CH2 |
72 |
- |
- |
|
S3_PH_CH2 |
487 |
231 |
413 |
|
S3_PH_CH3 |
338 |
147 |
295 |
|
|
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The values indicate the number of PSMs that the learning method could retrieve when considering a 1% FDR. The NN values were significantly better. The dashes indicate that the algorithm could not find a set of hits with 1% FDR, i.e., there is no point in the ROC curve corresponding to such an error rate. |
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Cerqueira et al. BMC Genomics 2012 13(Suppl 5):S4 doi:10.1186/1471-2164-13-S5-S4 |
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