The results of different approaches for using the motif features on independent test set
| Method . | ROC1 . | ROC50 . |
|---|---|---|
| PreHom-PCLM | 0.9857 | 0.9937 |
| 1D CNNa | 0.9712 | 0.9890 |
| AvgPoola | 0.9453 | 0.9770 |
| MaxPoola | 0.9372 | 0.9722 |
| Method . | ROC1 . | ROC50 . |
|---|---|---|
| PreHom-PCLM | 0.9857 | 0.9937 |
| 1D CNNa | 0.9712 | 0.9890 |
| AvgPoola | 0.9453 | 0.9770 |
| MaxPoola | 0.9372 | 0.9722 |
aLinearly concatenate the motif features.
The results of different approaches for using the motif features on independent test set
| Method . | ROC1 . | ROC50 . |
|---|---|---|
| PreHom-PCLM | 0.9857 | 0.9937 |
| 1D CNNa | 0.9712 | 0.9890 |
| AvgPoola | 0.9453 | 0.9770 |
| MaxPoola | 0.9372 | 0.9722 |
| Method . | ROC1 . | ROC50 . |
|---|---|---|
| PreHom-PCLM | 0.9857 | 0.9937 |
| 1D CNNa | 0.9712 | 0.9890 |
| AvgPoola | 0.9453 | 0.9770 |
| MaxPoola | 0.9372 | 0.9722 |
aLinearly concatenate the motif features.
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