Table 2 Performance comparison among different methods on datasets ATH948 and ZEA22133 (%)
From: PRPI-SC: an ensemble deep learning model for predicting plant lncRNA-protein interactions
Dataset | Method | Acc | Pre | Sn | Sp | MCC | AUC |
|---|---|---|---|---|---|---|---|
ATH948 | PRPI-SC | 88.9 | 91.4 | 84.2 | 91.8 | 78.1 | 95.0 |
IPMiner | 88.2 | 89.2 | 86.9 | 89.5 | 76.5 | 94.1 | |
RPISeq-RF | 75.6 | 76.2 | 75.2 | 73.0 | 79.4 | 90.2 | |
lncPro | 75.4 | 76.9 | 75.4 | 74.7 | 71.5 | 89.2 | |
ZEA22133 | PRPI-SC | 82.6 | 99.9 | 65.2 | 99.9 | 69.6 | 92.7 |
IPMiner | 68.7 | 69.6 | 66.5 | 70.9 | 37.5 | 84.6 | |
RPISeq-RF | 65.4 | 64.1 | 62.5 | 70.3 | 35.9 | 81.4 | |
lncPro | 60.3 | 61.3 | 60.8 | 69.6 | 30.9 | 80.8 |