Table 2

Summarized performances for all methods and datasets. |$F1_m$| and |$MCC_m$| are the best F1 and MCC along the precision-recall curve. |$\hat{AUC}_{pr}$| is the logarithmic ratio of the area under the precision-recall curve.

CELDMEAGAHSAATH
|$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$||$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$||$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$||$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$||$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$|
OC-SVM0.0120.0470.3420.0020.0150.2920.0130.0330.2400.0040.0140.1970.1530.1210.625
deepBN0.0090.0250.2200.0000.0020.0440.0010.0070.0020.0060.0160.2410.1430.1480.595
deeSOM0.0370.0630.3780.0750.1200.1660.0190.0230.3670.0280.0350.3650.1720.1870.649
miRNAss0.0300.0440.3570.0010.0050.0200.0080.0070.071---0.2120.1730.676
bb-DeepMir0.0280.0230.1900.0530.0150.0480.0090.0090.1090.0380.0500.4570.0850.0600.475
bb-deepMiRGene0.1030.0950.5670.0600.0400.3870.0580.0450.3360.0720.0310.5110.1950.1790.686
CELDMEAGAHSAATH
|$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$||$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$||$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$||$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$||$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$|
OC-SVM0.0120.0470.3420.0020.0150.2920.0130.0330.2400.0040.0140.1970.1530.1210.625
deepBN0.0090.0250.2200.0000.0020.0440.0010.0070.0020.0060.0160.2410.1430.1480.595
deeSOM0.0370.0630.3780.0750.1200.1660.0190.0230.3670.0280.0350.3650.1720.1870.649
miRNAss0.0300.0440.3570.0010.0050.0200.0080.0070.071---0.2120.1730.676
bb-DeepMir0.0280.0230.1900.0530.0150.0480.0090.0090.1090.0380.0500.4570.0850.0600.475
bb-deepMiRGene0.1030.0950.5670.0600.0400.3870.0580.0450.3360.0720.0310.5110.1950.1790.686
Table 2

Summarized performances for all methods and datasets. |$F1_m$| and |$MCC_m$| are the best F1 and MCC along the precision-recall curve. |$\hat{AUC}_{pr}$| is the logarithmic ratio of the area under the precision-recall curve.

CELDMEAGAHSAATH
|$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$||$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$||$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$||$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$||$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$|
OC-SVM0.0120.0470.3420.0020.0150.2920.0130.0330.2400.0040.0140.1970.1530.1210.625
deepBN0.0090.0250.2200.0000.0020.0440.0010.0070.0020.0060.0160.2410.1430.1480.595
deeSOM0.0370.0630.3780.0750.1200.1660.0190.0230.3670.0280.0350.3650.1720.1870.649
miRNAss0.0300.0440.3570.0010.0050.0200.0080.0070.071---0.2120.1730.676
bb-DeepMir0.0280.0230.1900.0530.0150.0480.0090.0090.1090.0380.0500.4570.0850.0600.475
bb-deepMiRGene0.1030.0950.5670.0600.0400.3870.0580.0450.3360.0720.0310.5110.1950.1790.686
CELDMEAGAHSAATH
|$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$||$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$||$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$||$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$||$F1_m$||$MCC_m$||$\hat{AUC}_{pr}$|
OC-SVM0.0120.0470.3420.0020.0150.2920.0130.0330.2400.0040.0140.1970.1530.1210.625
deepBN0.0090.0250.2200.0000.0020.0440.0010.0070.0020.0060.0160.2410.1430.1480.595
deeSOM0.0370.0630.3780.0750.1200.1660.0190.0230.3670.0280.0350.3650.1720.1870.649
miRNAss0.0300.0440.3570.0010.0050.0200.0080.0070.071---0.2120.1730.676
bb-DeepMir0.0280.0230.1900.0530.0150.0480.0090.0090.1090.0380.0500.4570.0850.0600.475
bb-deepMiRGene0.1030.0950.5670.0600.0400.3870.0580.0450.3360.0720.0310.5110.1950.1790.686
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