Table 3 Evaluation results of the DCB model based on One-hot encoding, RNA word embedding, Word2vec, and RGloVe
From: EMDLP: Ensemble multiscale deep learning model for RNA methylation site prediction
Modification type | Classifiers | AUROC | Acc (%) | Sn (%) | Sp (%) | MCC (%) | Pre (%) | F1 (%) | AUPRC |
|---|---|---|---|---|---|---|---|---|---|
m1A | DCBOne-hot | 0.9410 | 95.37 | 64.04 | 98.51 | 69.66 | 81.11 | 71.57 | 0.7812 |
DCBEmbedding | 0.9409 | 95.37 | 65.79 | 98.33 | 70.0 | 79.79 | 72.12 | 0.7715 | |
DCBword2vec | 0.9316 | 95.29 | 61.4 | 98.68 | 68.72 | 82.35 | 70.35 | 0.7349 | |
DCBRGloVe | 0.9468 | 95.45 | 64.04 | 98.6 | 70.12 | 82.02 | 71.92 | 0.7866 | |
m6A | DCBOne-hot | 0.8300 | 74.51 | 72.25 | 76.76 | 49.06 | 75.57 | 73.87 | 0.8080 |
DCBEmbedding | 0.8477 | 76.52 | 83.30 | 69.79 | 53.56 | 73.28 | 77.97 | 0.8272 | |
DCBword2vec | 0.8317 | 75.10 | 79.60 | 70.62 | 50.43 | 72.95 | 76.13 | 0.8126 | |
DCBRGloVe | 0.8486 | 76.36 | 84.2 | 68.57 | 53.41 | 72.72 | 78.04 | 0.8310 |