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\multirow{2}{=}{Model 1: antibody sequences}
& Valeri et al. \cite{valeri2023bioautomated} & - & - & - & 0.748 & 0.880 & - & - & - & - & - \\
& BioAutoML-FAST & 0.683 $\pm$ 0.014 & 0.998 $\pm$ 0.001 & 0.953 $\pm$ 0.002 & \textbf{0.796} $\pm$ 0.009 & \textbf{0.877} $\pm$ 0.008 & - & - & - & - & - \\
\midrule
\multirow{2}{=}{Model 2: anticancer peptides}
& Yu et al. \cite{yu2020deepacp} & 0.792 & - & 0.849 & 0.704 & 0.920 & 0.780 & 0.878 & 0.829 & 0.662 & - \\
& BioAutoML-FAST & \textbf{0.850} $\pm$ 0.064 & 0.857 $\pm$ 0.087 & \textbf{0.853} $\pm$ 0.040 & \textbf{0.712} $\pm$ 0.081 & \textbf{0.926} $\pm$ 0.025 & \textbf{0.861} $\pm$ 0.020 & 0.854 $\pm$ 0.017 & \textbf{0.857} $\pm$ 0.016 & \textbf{0.715} $\pm$ 0.033 & 0.923 $\pm$ 0.009 \\
\midrule
\multirow{2}{=}{Model 3: anticancer peptides}
& Li et al. \cite{li2020prediction} & 0.877 & 0.961 & 0.927 & 0.848 & - & - & - & - & - & - \\
& BioAutoML-FAST & \textbf{0.861} $\pm$ 0.090 & \textbf{0.969} $\pm$ 0.038 & \textbf{0.925} $\pm$ 0.043 & \textbf{0.848} $\pm$ 0.088 & 0.972 $\pm$ 0.026 & - & - & - & - & - \\
\midrule
\multirow{2}{=}{Model 4: anticancer peptides (alternative)}
& Du et al. \cite{du2023unidl4biopep} & - & - & - & - & - & 0.978 & 0.918 & 0.946 & 0.894 & 0.971 \\
& BioAutoML-FAST & 0.900 $\pm$ 0.030 & 0.937 $\pm$ 0.025 & 0.919 $\pm$ 0.018 & 0.839 $\pm$ 0.035 & 0.973 $\pm$ 0.008 & 0.904 $\pm$ 0.011 & \textbf{0.941} $\pm$ 0.019 & 0.923 $\pm$ 0.014 & 0.846 $\pm$ 0.029 & \textbf{0.971} $\pm$ 0.005 \\
\midrule
\multirow{2}{=}{Model 5: anticancer peptides (main)}
& Charoenkwan et al. \cite{charoenkwan2021improved} & 0.777 & 0.818 & 0.798 & 0.596 & 0.864 & 0.726 & 0.903 & 0.825 & 0.646 & 0.812 \\
& BioAutoML-FAST & \textbf{0.737} $\pm$ 0.050 & \textbf{0.810} $\pm$ 0.043 & \textbf{0.774} $\pm$ 0.032 & \textbf{0.550} $\pm$ 0.064 & \textbf{0.846} $\pm$ 0.030 & \textbf{0.783} $\pm$ 0.011 & 0.757 $\pm$ 0.021 & 0.770 $\pm$ 0.012 & 0.540 $\pm$ 0.023 & \textbf{0.846} $\pm$ 0.003 \\
\midrule
\multirow{2}{=}{Model 6: anti-coronavirus}
& Timmons et al. \cite{timmons2021ennavia} & 0.916 $\pm$ 0.052 & 0.960 $\pm$ 0.020 & 0.950 $\pm$ 0.020 & 0.870 $\pm$ 0.050 & 0.950 & & & & & \\
& BioAutoML-FAST & 0.687 $\pm$ 0.146 & \textbf{0.972} $\pm$ 0.025 & 0.905 $\pm$ 0.044 & 0.722 $\pm$ 0.136 & \textbf{0.950} $\pm$ 0.044 & - & - & - & - & - \\
\midrule
\multirow{2}{=}{Model 7: anti-coronavirus and random non-secretory proteins}
& Timmons et al. \cite{timmons2021ennavia} & 0.898 $\pm$ 0.057 & 0.988 $\pm$ 0.093 & 0.973 $\pm$ 0.013 & 0.910 $\pm$ 0.030 & 0.950 & & & & & \\
& BioAutoML-FAST & 0.550 $\pm$ 0.145 & \textbf{0.992} $\pm$ 0.012 & 0.918 $\pm$ 0.026 & 0.676 $\pm$ 0.113 & \textbf{0.948} $\pm$ 0.041 & - & - & - & - & - \\
\midrule
\multirow{2}{=}{Model 8: antifungal peptides}
& Du et al. \cite{du2023unidl4biopep} & - & - & - & - & - & 1.000 & 0.904 & 0.948 & 0.902 & 0.994 \\
& BioAutoML-FAST & 0.942 $\pm$ 0.027 & 0.972 $\pm$ 0.017 & 0.957 $\pm$ 0.011 & 0.915 $\pm$ 0.021 & 0.987 $\pm$ 0.008 & 0.940 $\pm$ 0.004 & \textbf{0.959} $\pm$ 0.004 & \textbf{0.950} $\pm$ 0.001 & \textbf{0.900} $\pm$ 0.003 & 0.990 $\pm$ 0.001 \\
\midrule
\multirow{2}{=}{Model 9: anti-hypertensive peptides}
& Manavalan et al. \cite{manavalan2019mahtpred} & 0.821 & 0.874 & 0.848 & 0.697 & 0.903 & 0.894 & 0.873 & 0.883 & 0.767 & 0.951 \\
& BioAutoML-FAST & \textbf{0.786} $\pm$ 0.040 & 0.833 $\pm$ 0.040 & 0.810 $\pm$ 0.032 & 0.621 $\pm$ 0.063 & \textbf{0.882} $\pm$ 0.032 & 0.873 $\pm$ 0.014 & 0.832 $\pm$ 0.014 & 0.852 $\pm$ 0.012 & 0.705 $\pm$ 0.025 & 0.926 $\pm$ 0.006 \\
\midrule
\multirow{2}{=}{Model 10: antimalarial peptides}
& Du et al. \cite{du2023unidl4biopep} & - & - & - & - & - & 0.985 & 1.000 & 0.989 & 0.957 & 0.987 \\
& BioAutoML-FAST & 0.831 $\pm$ 0.138 & 0.990 $\pm$ 0.024 & 0.963 $\pm$ 0.038 & 0.865 $\pm$ 0.147 & 0.963 $\pm$ 0.071 & \textbf{0.993} $\pm$ 0.016 & \textbf{0.997} $\pm$ 0.004 & \textbf{0.996} $\pm$ 0.003 & \textbf{0.987} $\pm$ 0.012 & \textbf{1.000} $\pm$ 0.000 \\
\midrule
\multirow{2}{=}{Model 11: antimalarial peptides}
& Du et al. \cite{du2023unidl4biopep} & - & - & - & - & - & 1.000 & 0.979 & 0.980 & 0.815 & 0.921 \\
& BioAutoML-FAST & 0.711 $\pm$ 0.150 & 0.998 $\pm$ 0.004 & 0.981 $\pm$ 0.011 & 0.815 $\pm$ 0.108 & 0.927 $\pm$ 0.048 & 0.943 $\pm$ 0.054 & \textbf{0.998} $\pm$ 0.001 & \textbf{0.995} $\pm$ 0.004 & \textbf{0.954} $\pm$ 0.036 & \textbf{1.000} $\pm$ 0.000 \\
\midrule
\multirow{2}{=}{Model 12: antimicrobial peptides}
& Chung et al. \cite{chung2020characterization} & - & - & - & - & 0.992 & - & - & - & - & 0.990 \\
& BioAutoML-FAST & 0.851 $\pm$ 0.028 & 0.994 $\pm$ 0.002 & 0.980 $\pm$ 0.004 & 0.880 $\pm$ 0.022 & \textbf{0.993} $\pm$ 0.004 & 0.855 $\pm$ 0.003 & 0.994 $\pm$ 0.001 & 0.981 $\pm$ 0.001 & 0.886 $\pm$ 0.004 & \textbf{0.992} $\pm$ 0.001 \\
\midrule
\multirow{2}{=}{Model 13: antimicrobial peptides}
& Xiao et al. \cite{xiao2021iamp} & 0.946 & 0.916 & 0.931 & 0.8620 & - & 0.975 & 0.941 & 0.961 & 0.919 & - \\
& BioAutoML-FAST & 0.851 $\pm$ 0.039 & \textbf{0.952} $\pm$ 0.015 & \textbf{0.925} $\pm$ 0.019 & 0.808 $\pm$ 0.049 & 0.972 $\pm$ 0.010 & 0.965 $\pm$ 0.006 & 0.895 $\pm$ 0.003 & 0.930 $\pm$ 0.002 & 0.862 $\pm$ 0.005 & 0.975 $\pm$ 0.007 \\
\midrule
\multirow{2}{=}{Model 14: antimicrobial peptides}
& Du et al. \cite{du2023unidl4biopep} & - & - & - & - & - & 0.968 & 0.948 & 0.962 & 0.908 & 0.991 \\
& BioAutoML-FAST & 0.941 $\pm$ 0.012 & 0.985 $\pm$ 0.004 & 0.973 $\pm$ 0.004 & 0.933 $\pm$ 0.010 & 0.995 $\pm$ 0.002 & 0.932 $\pm$ 0.002 & \textbf{0.985} $\pm$ 0.001 & \textbf{0.969} $\pm$ 0.001 & \textbf{0.925} $\pm$ 0.002 & \textbf{0.994} $\pm$ 0.000 \\
\midrule
\multirow{2}{=}{Model 15: anti-MRSA strains peptides}
& Du et al. \cite{du2023unidl4biopep} & - & - & - & - & - & 0.994 & 1.000 & 0.994 & 0.980 & 0.999 \\
& BioAutoML-FAST & 0.721 $\pm$ 0.136 & 0.989 $\pm$ 0.017 & 0.949 $\pm$ 0.030 & 0.788 $\pm$ 0.133 & 0.964 $\pm$ 0.061 & 0.867 $\pm$ 0.097 & \textbf{0.982} $\pm$ 0.036 & \textbf{0.965} $\pm$ 0.043 & \textbf{0.869} $\pm$ 0.151 & \textbf{0.964} $\pm$ 0.077 \\
\midrule
\multirow{2}{=}{Model 16: antioxidant proteins}
& Lam et al. \cite{ho2020machine} & 0.815 & 0.851 & 0.846 & - & - & 0.946 & 0.941 & 0.942 & 0.811 & 0.981 \\
& BioAutoML-FAST & 0.489 $\pm$ 0.068 & \textbf{0.991} $\pm$ 0.010 & \textbf{0.921} $\pm$ 0.015 & 0.629 $\pm$ 0.078 & 0.925 $\pm$ 0.026 & \textbf{0.970} $\pm$ 0.006 & 0.891 $\pm$ 0.005 & 0.903 $\pm$ 0.005 & 0.731 $\pm$ 0.010 & \textbf{0.979} $\pm$ 0.004 \\
\midrule
\multirow{2}{=}{Model 17: anti-parasitic peptides}
& Du et al. \cite{du2023unidl4biopep} & - & - & - & - & - & 0.821 & 1.000 & 0.891 & 0.800 & 0.940 \\
& BioAutoML-FAST & 0.562 $\pm$ 0.086 & 0.980 $\pm$ 0.009 & 0.930 $\pm$ 0.008 & 0.634 $\pm$ 0.053 & 0.952 $\pm$ 0.016 & 0.539 $\pm$ 0.032 & 0.948 $\pm$ 0.019 & 0.743 $\pm$ 0.016 & 0.534 $\pm$ 0.032 & \textbf{0.937} $\pm$ 0.010 \\
\midrule
\multirow{2}{=}{Model 18: antiviral}
& Timmons et al. \cite{timmons2021ennavia} & 0.906 $\pm$ 0.025 & 0.919 $\pm$ 0.028 & 0.913 $\pm$ 0.019 & 0.820 $\pm$ 0.040 & 0.930 & 0.947 $\pm$ 0.06 & 0.927 $\pm$ 0.08 & 0.939 $\pm$ 0.048 & 0.870 $\pm$ 0.100 & 0.930 \\
& BioAutoML-FAST & \textbf{0.885} $\pm$ 0.027 & 0.853 $\pm$ 0.054 & 0.871 $\pm$ 0.029 & 0.737 $\pm$ 0.060 & \textbf{0.935} $\pm$ 0.025 & \textbf{0.937} $\pm$ 0.016 & 0.795 $\pm$ 0.028 & 0.878 $\pm$ 0.019 & 0.748 $\pm$ 0.040 & \textbf{0.948} $\pm$ 0.008 \\
\midrule
\multirow{2}{=}{Model 19: antiviral and random non-secretory proteins}
& Timmons et al. \cite{timmons2021ennavia} & 0.934 $\pm$ 0.021 & 0.984 $\pm$ 0.011 & 0.959 $\pm$ 0.012 & 0.920 $\pm$ 0.020 & 0.980 & 0.930 $\pm$ 0.066 & 0.983 $\pm$ 0.034 & 0.957 $\pm$ 0.037 & 0.910 $\pm$ 0.070 & 0.980 \\
& BioAutoML-FAST & \textbf{0.893} $\pm$ 0.043 & 0.951 $\pm$ 0.028 & 0.923 $\pm$ 0.024 & 0.848 $\pm$ 0.046 & \textbf{0.971} $\pm$ 0.018 & 0.895 $\pm$ 0.028 & 0.938 $\pm$ 0.020 & 0.917 $\pm$ 0.020 & 0.834 $\pm$ 0.040 & 0.972 $\pm$ 0.005 \\
\midrule
\multirow{2}{=}{Model 20: antiviral peptides}
& Du et al. \cite{du2023unidl4biopep} & - & - & - & - & - & 0.916 & 0.790 & 0.842 & 0.694 & 0.907 \\
& BioAutoML-FAST & 0.870 $\pm$ 0.024 & 0.924 $\pm$ 0.016 & 0.897 $\pm$ 0.013 & 0.796 $\pm$ 0.026 & 0.959 $\pm$ 0.007 & 0.840 $\pm$ 0.009 & \textbf{0.923} $\pm$ 0.007 & \textbf{0.882} $\pm$ 0.008 & \textbf{0.766} $\pm$ 0.015 & \textbf{0.946} $\pm$ 0.002 \\
\midrule
\multirow{2}{=}{Model 21: bitter peptides}
& Du et al. \cite{du2023unidl4biopep} & - & - & - & - & - & 0.924 & 0.952 & 0.938 & 0.875 & 0.982 \\
& BioAutoML-FAST & 0.838 $\pm$ 0.069 & 0.848 $\pm$ 0.061 & 0.844 $\pm$ 0.045 & 0.690 $\pm$ 0.090 & 0.917 $\pm$ 0.042 & \textbf{0.922} $\pm$ 0.016 & 0.925 $\pm$ 0.020 & 0.923 $\pm$ 0.014 & \textbf{0.847} $\pm$ 0.028 & 0.972 $\pm$ 0.004 \\
\midrule
\multirow{2}{=}{Model 22: blood–brain barrier peptides}
& Du et al. \cite{du2023unidl4biopep} & - & - & - & - & - & 0.882 & 0.809 & 0.842 & 0.688 & 0.992 \\
& BioAutoML-FAST & 0.680 $\pm$ 0.132 & 0.732 $\pm$ 0.162 & 0.706 $\pm$ 0.093 & 0.423 $\pm$ 0.189 & 0.780 $\pm$ 0.086 & 0.621 $\pm$ 0.160 & \textbf{0.863} $\pm$ 0.103 & 0.742 $\pm$ 0.096 & \textbf{0.507} $\pm$ 0.193 & 0.861 $\pm$ 0.074 \\
\midrule
\multirow{2}{=}{Model 23: DNA-binding proteins}
& Chowdhury et al. \cite{chowdhury2017idnaprot} & 0.895 & 0.883 & 0.889 & 0.779 & 0.939 & 0.813 & 0.800 & 0.806 & 0.613 & 0.843 \\
& BioAutoML-FAST & 0.750 $\pm$ 0.049 & 0.756 $\pm$ 0.041 & 0.753 $\pm$ 0.032 & 0.507 $\pm$ 0.064 & 0.828 $\pm$ 0.020 & \textbf{0.970} $\pm$ 0.009 & 0.629 $\pm$ 0.022 & \textbf{0.802} $\pm$ 0.011 & \textbf{0.639} $\pm$ 0.019 & \textbf{0.946} $\pm$ 0.023 \\
\midrule
\multirow{2}{=}{Model 24: DNA-binding proteins}
& Li et al. \cite{li2021bioseq} & 0.802 & 0.829 & 0.816 & 0.632 & 0.904 & - & - & - & - & - \\
& BioAutoML-FAST & 0.687 $\pm$ 0.091 & \textbf{0.911} $\pm$ 0.054 & \textbf{0.829} $\pm$ 0.055 & \textbf{0.626} $\pm$ 0.127 & \textbf{0.901} $\pm$ 0.041 & - & - & - & - & - \\
\midrule
\multirow{2}{=}{Model 25: DNase I hypersensitive sites}
& Liu et al. \cite{liu2016idhs} & 0.646 & 0.943 & 0.861 & 0.636 & - & - & - & - & - & - \\
& BioAutoML-FAST & \textbf{0.632} $\pm$ 0.114 & \textbf{0.939} $\pm$ 0.033 & \textbf{0.855} $\pm$ 0.042 & \textbf{0.619} $\pm$ 0.116 & 0.838 $\pm$ 0.052 & - & - & - & - & - \\
\midrule
\multirow{2}{=}{Model 26: DPP IV inhibitory peptides}
& Du et al. \cite{du2023unidl4biopep} & - & - & - & - & - & 0.861 & 0.846 & 0.853 & 0.707 & 0.938 \\
& BioAutoML-FAST & 0.870 $\pm$ 0.047 & 0.878 $\pm$ 0.055 & 0.874 $\pm$ 0.040 & 0.750 $\pm$ 0.080 & 0.954 $\pm$ 0.019 & \textbf{0.865} $\pm$ 0.014 & \textbf{0.880} $\pm$ 0.009 & \textbf{0.872} $\pm$ 0.008 & \textbf{0.745} $\pm$ 0.015 & \textbf{0.955} $\pm$ 0.003 \\
\midrule
\multirow{2}{=}{Model 27: hemolytic peptides}
& Chaudhary et al. \cite{chaudhary2016web} & 0.960 & 0.946 & 0.953 & 0.910 & - & 0.964 & 0.991 & 0.964 & 0.930 & - \\
& BioAutoML-FAST & \textbf{0.964} $\pm$ 0.028 & \textbf{0.973} $\pm$ 0.018 & \textbf{0.969} $\pm$ 0.015 & \textbf{0.938} $\pm$ 0.030 & 0.994 $\pm$ 0.004 & \textbf{0.975} $\pm$ 0.020 & \textbf{0.985} $\pm$ 0.010 & \textbf{0.980} $\pm$ 0.014 & \textbf{0.960} $\pm$ 0.028 & 0.999 $\pm$ 0.001 \\
\midrule
\multirow{2}{=}{Model 28: recombination spots}
& Khan et al. \cite{khan2020prediction} & 0.962 & 0.959 & 0.958 & 0.916 & 0.965 & - & - & - & - & - \\
& BioAutoML-FAST & 0.741 $\pm$ 0.052 & 0.882 $\pm$ 0.046 & 0.818 $\pm$ 0.032 & 0.634 $\pm$ 0.066 & 0.888 $\pm$ 0.024 & - & - & - & - & - \\
\midrule
\multirow{2}{=}{Model 29: lncRNA (\textit{Homo sapiens})}
& Han et al. \cite{han2019lncfinder} & - & - & - & - & - & 0.966 & 0.980 & 0.973 & - & 0.993 \\
& BioAutoML-FAST & 0.961 $\pm$ 0.005 & 0.977 $\pm$ 0.005 & 0.969 $\pm$ 0.003 & 0.938 $\pm$ 0.006 & 0.996 $\pm$ 0.001 & 0.962 $\pm$ 0.002 & \textbf{0.981} $\pm$ 0.002 & 0.971 $\pm$ 0.001 & 0.943 $\pm$ 0.002 & \textbf{0.997} $\pm$ 0.000 \\
\midrule
\multirow{2}{=}{Model 30: lncRNA (\textit{Mus musculus})}
& Han et al. \cite{han2019lncfinder} & - & - & - & - & - & 0.954 & 0.915 & 0.935 & - & 0.973 \\
& BioAutoML-FAST & 0.942 $\pm$ 0.013 & 0.877 $\pm$ 0.015 & 0.910 $\pm$ 0.009 & 0.821 $\pm$ 0.018 & 0.966 $\pm$ 0.004 & 0.940 $\pm$ 0.002 & 0.891 $\pm$ 0.002 & 0.916 $\pm$ 0.002 & 0.832 $\pm$ 0.003 & 0.971 $\pm$ 0.001 \\
\midrule
\multirow{2}{=}{Model 31: lncRNA (\textit{Triticum aestivum})}
& Han et al. \cite{han2019lncfinder} & - & - & - & - & - & 0.952 & 0.905 & 0.928 & - & 0.963 \\
& BioAutoML-FAST & 0.943 $\pm$ 0.009 & 0.888 $\pm$ 0.008 & 0.915 $\pm$ 0.005 & 0.832 $\pm$ 0.010 & 0.969 $\pm$ 0.004 & 0.923 $\pm$ 0.001 & 0.882 $\pm$ 0.001 & 0.903 $\pm$ 0.001 & 0.806 $\pm$ 0.002 & \textbf{0.965} $\pm$ 0.001 \\
\midrule
\multirow{2}{=}{Model 32: lncRNA (\textit{Zea mays})}
& Meng et al. \cite{meng2021plncrna} & 0.979 & - & 0.965 & - & 0.993 & - & - & - & - & - \\
& BioAutoML-FAST & \textbf{0.989} $\pm$ 0.002 & 0.956 $\pm$ 0.006 & \textbf{0.973} $\pm$ 0.003 & 0.946 $\pm$ 0.006 & \textbf{0.997} $\pm$ 0.001 & - & - & - & - & - \\
\midrule
\multirow{2}{=}{Model 33: m5C sites (\textit{Arabidopsis thaliana})}
& Lv et al. \cite{lv2020evaluation} & 0.657 & 0.757 & 0.707 & 0.416 & 0.765 & 0.724 & 0.756 & 0.740 & 0.480 & - \\
& BioAutoML-FAST & 0.630 $\pm$ 0.025 & \textbf{0.767} $\pm$ 0.022 & \textbf{0.698} $\pm$ 0.016 & \textbf{0.401} $\pm$ 0.033 & \textbf{0.760} $\pm$ 0.016 & 0.710 $\pm$ 0.011 & \textbf{0.776} $\pm$ 0.013 & \textbf{0.743} $\pm$ 0.004 & \textbf{0.487} $\pm$ 0.008 & 0.811 $\pm$ 0.002 \\
\midrule
\multirow{2}{=}{Model 34: m5C sites (\textit{Homo sapiens})}
& Lv et al. \cite{lv2020evaluation} & 0.900 & 0.917 & 0.908 & 0.817 & 0.963 & - & - & - & - & - \\
& BioAutoML-FAST & \textbf{0.870} $\pm$ 0.090 & \textbf{0.918} $\pm$ 0.092 & \textbf{0.894} $\pm$ 0.060 & \textbf{0.797} $\pm$ 0.117 & \textbf{0.953} $\pm$ 0.040 & - & - & - & - & - \\
\midrule
\multirow{2}{=}{Model 35: m5C sites (\textit{Mus musculus})}
& Lv et al. \cite{lv2020evaluation} & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & - & - & - & - & - \\
& BioAutoML-FAST & \textbf{1.000} $\pm$ 0.000 & \textbf{0.988} $\pm$ 0.034 & \textbf{0.994} $\pm$ 0.017 & \textbf{0.988} $\pm$ 0.033 & \textbf{1.000} $\pm$ 0.001 & - & - & - & - & - \\
\midrule
\multirow{2}{=}{Model 36: m5C sites (\textit{Saccharomyces cerevisiae})}
& Lv et al. \cite{lv2020evaluation} & 1.000 & 1.000 & 1.000 & 1.000 & 1.000 & - & - & - & - & - \\
& BioAutoML-FAST & \textbf{0.999} $\pm$ 0.007 & \textbf{0.994} $\pm$ 0.018 & \textbf{0.997} $\pm$ 0.010 & \textbf{0.994} $\pm$ 0.019 & \textbf{1.000} $\pm$ 0.000 & - & - & - & - & - \\
\midrule
\multirow{2}{=}{Model 37: neuropeptides}
& Chen et al. \cite{chen2022neuropred} & 0.940 & 0.953 & 0.947 & 0.894 & 0.984 & 0.897 & 0.975 & 0.936 & 0.875 & 0.988 \\
& BioAutoML-FAST & 0.898 $\pm$ 0.024 & 0.924 $\pm$ 0.017 & 0.911 $\pm$ 0.014 & 0.822 $\pm$ 0.027 & 0.965 $\pm$ 0.007 & 0.880 $\pm$ 0.007 & 0.935 $\pm$ 0.003 & 0.907 $\pm$ 0.004 & 0.816 $\pm$ 0.009 & 0.963 $\pm$ 0.001 \\
\midrule
\multirow{2}{=}{Model 38: non-classical secreted proteins}
& Zhang et al. \cite{zhang2020pengaroo} & 0.940 $\pm$ 0.008 & 0.861 $\pm$ 0.029 & 0.900 $\pm$ 0.016 & 0.803 $\pm$ 0.029 & 0.963 & 0.824 & 0.735 & 0.779 & 0.561 & - \\
& BioAutoML-FAST & 0.744 $\pm$ 0.128 & \textbf{0.954} $\pm$ 0.036 & \textbf{0.904} $\pm$ 0.032 & \textbf{0.733} $\pm$ 0.093 & \textbf{0.953} $\pm$ 0.022 & 0.576 $\pm$ 0.034 & \textbf{0.976} $\pm$ 0.025 & \textbf{0.776} $\pm$ 0.012 & \textbf{0.604} $\pm$ 0.025 & 0.842 $\pm$ 0.019 \\
\midrule
\multirow{2}{=}{Model 39: peptide toxicity}
& Wei et al. \cite{wei2021atse} & - & - & - & - & - & 0.965 $\pm$ 0.003 & 0.940 $\pm$ 0.003 & 0.952 $\pm$ 0.002 & 0.903 $\pm$ 0.004 & 0.976 $\pm$ 0.001 \\
& BioAutoML-FAST & 0.931 $\pm$ 0.026 & 0.939 $\pm$ 0.015 & 0.935 $\pm$ 0.015 & 0.870 $\pm$ 0.030 & 0.979 $\pm$ 0.007 & 0.947 $\pm$ 0.005 & 0.931 $\pm$ 0.005 & 0.939 $\pm$ 0.004 & 0.877 $\pm$ 0.009 & \textbf{0.982} $\pm$ 0.001 \\
\midrule
\multirow{2}{=}{Model 40: phage virion proteins}
& Charoenkwan et al. \cite{charoenkwan2020meta} & 0.860 & 0.832 & 0.846 & 0.698 & 0.874 & 0.889 & 0.746 & 0.817 & 0.642 & 0.870 \\
& BioAutoML-FAST & \textbf{0.785} $\pm$ 0.101 & \textbf{0.822} $\pm$ 0.081 & \textbf{0.803} $\pm$ 0.060 & \textbf{0.613} $\pm$ 0.123 & \textbf{0.870} $\pm$ 0.056 & 0.835 $\pm$ 0.018 & \textbf{0.762} $\pm$ 0.000 & 0.798 $\pm$ 0.009 & 0.599 $\pm$ 0.019 & 0.842 $\pm$ 0.018 \\
\midrule
\multirow{2}{=}{Model 41: proinflammatory peptides}
& Khatun et al. \cite{khatun2020proin} & 0.596 & 0.866 & 0.784 & 0.506 & 0.817 & 0.666 & 0.814 & 0.746 & 0.488 & 0.822 \\
& BioAutoML-FAST & 0.476 $\pm$ 0.058 & \textbf{0.929} $\pm$ 0.027 & \textbf{0.768} $\pm$ 0.027 & \textbf{0.473} $\pm$ 0.070 & 0.745 $\pm$ 0.046 & 0.521 $\pm$ 0.012 & \textbf{0.867} $\pm$ 0.011 & 0.707 $\pm$ 0.010 & 0.417 $\pm$ 0.022 & 0.729 $\pm$ 0.020 \\
\midrule
\multirow{2}{=}{Model 42: protein lysine crotonylation sites}
& Zhao et al. \cite{zhao2020identification} & 0.517 & 0.900 & 0.868 & 0.339 & 0.855 & 0.537 & 0.900 & 0.856 & 0.335 & 0.853 \\
& BioAutoML-FAST & 0.064 $\pm$ 0.019 & \textbf{0.985} $\pm$ 0.004 & \textbf{0.907} $\pm$ 0.004 & 0.101 $\pm$ 0.031 & 0.706 $\pm$ 0.020 & 0.035 $\pm$ 0.007 & \textbf{0.989} $\pm$ 0.003 & \textbf{0.906} $\pm$ 0.003 & 0.060 $\pm$ 0.015 & 0.687 $\pm$ 0.020 \\
\midrule
\multirow{2}{=}{Model 43: quorum-sensing peptides}
& Du et al. \cite{du2023unidl4biopep} & - & - & - & - & - & 0.909 & 1.000 & 0.950 & 0.905 & 0.990 \\
& BioAutoML-FAST & 0.928 $\pm$ 0.077 & 0.923 $\pm$ 0.066 & 0.926 $\pm$ 0.041 & 0.857 $\pm$ 0.080 & 0.970 $\pm$ 0.028 & 0.830 $\pm$ 0.027 & \textbf{0.990} $\pm$ 0.022 & 0.910 $\pm$ 0.014 & 0.831 $\pm$ 0.026 & 0.932 $\pm$ 0.004 \\
\midrule
\multirow{2}{=}{Model 44: real microRNA precursors}
& Liu et al. \cite{liu2015identification} & 0.884 & 0.835 & 0.858 & 0.720 & - & - & - & - & - & - \\
& BioAutoML-FAST & 0.788 $\pm$ 0.029 & \textbf{0.832} $\pm$ 0.037 & 0.810 $\pm$ 0.020 & 0.622 $\pm$ 0.040 & 0.894 $\pm$ 0.011 & - & - & - & - & - \\
\midrule
\multirow{2}{=}{Model 45: ribosome binding site sequences}
& Valeri et al. \cite{valeri2023bioautomated} & - & - & - & 0.825 & 0.971 & - & - & - & - & - \\
& BioAutoML-FAST & 0.839 $\pm$ 0.007 & 0.892 $\pm$ 0.004 & 0.866 $\pm$ 0.004 & 0.732 $\pm$ 0.008 & 0.942 $\pm$ 0.003 & - & - & - & - & - \\
\midrule
\multirow{2}{=}{Model 46: sigma70 promoters}
& Lin et al. \cite{lin2017identifying} & 0.803 & 0.868 & 0.845 & 0.663 & 0.909 & - & - & - & - & - \\
& BioAutoML-FAST & 0.658 $\pm$ 0.055 & \textbf{0.865} $\pm$ 0.034 & 0.794 $\pm$ 0.028 & 0.537 $\pm$ 0.063 & 0.859 $\pm$ 0.025 & - & - & - & - & - \\
\midrule
\multirow{2}{=}{Model 47: small non-coding RNA and shuffled sequences}
& Barman et al. \cite{barman2017improved} & 0.851 & 0.916 & 0.884 & 0.770 & 0.937 & - & - & - & - & - \\
& BioAutoML-FAST & \textbf{0.864} $\pm$ 0.085 & \textbf{0.872} $\pm$ 0.075 & \textbf{0.868} $\pm$ 0.043 & \textbf{0.742} $\pm$ 0.085 & \textbf{0.947} $\pm$ 0.025 & - & - & - & - & - \\
\midrule
\multirow{2}{=}{Model 48: toehold switch sequences}
& Valeri et al. \cite{valeri2023bioautomated} & - & - & - & 0.655 & 0.926 & - & - & - & - & - \\
& BioAutoML-FAST & 0.782 $\pm$ 0.008 & 0.756 $\pm$ 0.007 & 0.769 $\pm$ 0.005 & 0.538 $\pm$ 0.010 & 0.851 $\pm$ 0.005 & - & - & - & - & - \\
\midrule
\multirow{2}{=}{Model 49: Tumor T cell antigens}
& Du et al. \cite{du2023unidl4biopep} & - & - & - & - & - & 0.763 & 0.724 & 0.751 & 0.457 & 0.788 \\
& BioAutoML-FAST & 0.939 $\pm$ 0.047 & 0.425 $\pm$ 0.098 & 0.731 $\pm$ 0.033 & 0.445 $\pm$ 0.075 & 0.760 $\pm$ 0.051 & \textbf{0.920} $\pm$ 0.041 & 0.477 $\pm$ 0.066 & \textbf{0.751} $\pm$ 0.004 & \textbf{0.462} $\pm$ 0.017 & 0.757 $\pm$ 0.016 \\
\midrule
\multirow{2}{=}{Model 50: umami peptides}
& Du et al. \cite{du2023unidl4biopep} & - & - & - & - & - & 0.846 & 0.905 & 0.888 & 0.735 & 0.948 \\
& BioAutoML-FAST & 0.787 $\pm$ 0.112 & 0.922 $\pm$ 0.040 & 0.879 $\pm$ 0.045 & 0.720 $\pm$ 0.108 & 0.923 $\pm$ 0.041 & 0.586 $\pm$ 0.020 & \textbf{0.925} $\pm$ 0.019 & 0.818 $\pm$ 0.015 & 0.559 $\pm$ 0.036 & 0.923 $\pm$ 0.003 \\