CircSSNN: circRNA-binding site prediction via sequence self-attention neural networks with pre-normalization

Cao, Chao; Yang, Shuhong; Li, Mengli; Li, Chungui

doi:10.1186/s12859-023-05352-7

BMC Bioinformatics

Table 1 The AUC of competing methods on 37 circRNA datasets

From: CircSSNN: circRNA-binding site prediction via sequence self-attention neural networks with pre-normalization

Dataset	CircSSNN	HCRNet	CRBPDL	iCircRBP-DHN	DeCban	PASSION	CircRB	circ2CBA
AGO1	0.92 ± 0.004	0.929 ± 0.017	0.9232	0.898 ± 0.003	0.873	0.909 ± 0.003	0.750 ± 0.003	0.9009
AGO2	0.847 ± 0.037	0.856 ± 0.236	0.8233	0.797 ± 0.004	0.846	0.822 ± 0.003	0.624 ± 0.008	0.8029
AGO3	0.949 ± 0.006	0.941 ± 0.034	0.9472	0.920 ± 0.016	0.89	0.909 ± 0.008	0.718 ± 0.004	0.9105
ALKBH5	0.99 ± 0.033	0.989 ± 0.013	0.9952	0.979 ± 0.004	0.919	0.752 ± 0.030	0.593 ± 0.009	0.7696
AUF1	0.987 ± 0.003	0.988 ± 0.013	0.981	0.985 ± 0.002	0.959	0.979 ± 0.003	0.938 ± 0.003	0.9888
C17ORF85	0.982 ± 0.05	0.981 ± 0.047	0.9881	0.987 ± 0.002	0.915	0.860 ± 0.021	0.634 ± 0.021	–
C22ORF28	0.947 ± 0.073	0.936 ± 0.101	0.9088	0.913 ± 0.004	0.877	0.894 ± 0.008	0.731 ± 0.005	–
CAPRIN1	0.912 ± 0.007	0.904 ± 0.006	0.8765	0.858 ± 0.012	0.887	0.860 ± 0.009	0.685 ± 0.002	–
DGCR8	0.915 ± 0.054	0.917 ± 0.305	0.9236	0.906 ± 0.002	0.847	0.917 ± 0.002	0.770 ± 0.002	–
EIF4A3	0.847 ± 0.03	0.848 ± 0.035	0.853	0.799 ± 0.003	0.819	0.823 ± 0.004	0.662 ± 0.002	–
EWSR1	0.955 ± 0.011	0.949 ± 0.086	0.9443	0.942 ± 0.004	0.938	0.938 ± 0.006	0.805 ± 0.004	–
FMRP	0.925 ± 0.027	0.933 ± 0.02	0.8966	0.892 ± 0.002	0.845	0.900 ± 0.002	0.737 ± 0.001	–
FOX2	0.962 ± 0.005	0.961 ± 0.136	0.9618	0.958 ± 0.005	0.936	0.830 ± 0.034	0.535 ± 0.003	–
FUS	0.888 ± 0.002	0.888 ± 0.008	0.8618	0.855 ± 0.004	0.842	0.859 ± 0.002	0.697 ± 0.004	–
FXR1	0.995 ± 0.013	0.984 ± 0.019	0.9948	0.994 ± 0.001	0.934	0.959 ± 0.009	0.838 ± 0.015	0.9579
FXR2	0.966 ± 0.043	0.96 ± 0.023	0.9518	0.939 ± 0.009	0.909	0.941 ± 0.003	0.774 ± 0.003	–
HNRNPC	0.973 ± 0.002	0.981 ± 0.002	0.9771	0.977 ± 0.001	0.97	0.976 ± 0.001	0.941 ± 0.003	–
HUR	0.908 ± 0.011	0.906 ± 0.009	0.8758	0.867 ± 0.005	0.841	0.879 ± 0.006	0.666 ± 0.001	0.8741
IGF2BP1	0.889 ± 0.019	0.886 ± 0.021	0.8554	0.843 ± 0.002	0.859	0.845 ± 0.003	0.679 ± 0.003	–
IGF2BP2	0.871 ± 0.004	0.875 ± 0.082	0.8426	0.831 ± 0.004	0.886	0.827 ± 0.009	0.644 ± 0.009	–
IGF2BP3	0.867 ± 0.006	0.871 ± 0.298	0.8229	0.816 ± 0.004	0.867	0.831 ± 0.003	0.635 ± 0.004	–
LIN28A	0.897 ± 0.014	0.901 ± 0.071	0.8751	0.857 ± 0.007	0.871	0.875 ± 0.005	0.671 ± 0.003	–
LIN28B	0.919 ± 0.038	0.912 ± 0.178	0.9014	0.892 ± 0.004	0.893	0.889 ± 0.005	0.731 ± 0.003	–
METTL3	0.892 ± 0.147	0.901 ± 0.365	0.8649	0.852 ± 0.009	0.918	0.878 ± 0.010	0.731 ± 0.006	–
MOV10	0.87 ± 0.207	0.866 ± 0.498	0.8674	0.838 ± 0.006	0.958	0.845 ± 0.005	0.698 ± 0.004	–
PTB	0.854 ± 0.042	0.851 ± 0.096	0.8347	0.822 ± 0.006	0.873	0.829 ± 0.004	0.663 ± 0.002	–
PUM2	0.974 ± 0.053	0.979 ± 0.041	0.9758	0.970 ± 0.004	0.959	0.952 ± 0.004	0.854 ± 0.001	–
QKI	0.988 ± 0.005	0.989 ± 0.013	0.9879	0.971 ± 0.006	0.933	0.927 ± 0.005	0.807 ± 0.006	–
SFRS1	0.981 ± 0.001	0.979 ± 0.002	0.9684	0.964 ± 0.000	0.891	0.965 ± 0.003	0.836 ± 0.003	–
TAF15	0.996 ± 0.001	0.996 ± 0.003	0.9945	0.992 ± 0.002	0.925	0.967 ± 0.002	0.883 ± 0.006	0.9851
TDP43	0.932 ± 0.043	0.939 ± 0.058	0.9336	0.926 ± 0.002	0.964	0.934 ± 0.002	0.829 ± 0.004	–
TIA1	0.982 ± 0.091	0.972 ± 0.009	0.9666	0.961 ± 0.004	0.967	0.935 ± 0.006	0.827 ± 0.008	–
TIAL1	0.936 ± 0.067	0.921 ± 0.241	0.9249	0.917 ± 0.003	0.965	0.906 ± 0.003	0.820 ± 0.004	–
TNRC6	0.952 ± 0.016	0.953 ± 0.124	0.9797	0.967 ± 0.002	0.925	0.785 ± 0.010	0.550 ± 0.017	–
U2AF65	0.934 ± 0.036	0.932 ± 0.104	0.9306	0.926 ± 0.002	0.931	0.930 ± 0.002	0.787 ± 0.003	–
WTAP	0.973 ± 0.007	0.983 ± 0.86	0.9713	0.967 ± 0.006	0.934	0.794 ± 0.069	0.621 ± 0.025	–
ZC3H7B	0.855 ± 0.06	0.863 ± 0.021	0.8151	0.804 ± 0.003	0.909	0.804 ± 0.005	0.634 ± 0.006	–
AVG	0.931 ± 0.054	0.93 ± 0.071	0.9188	0.908 ± 0.060	0.9047	0.884 ± 0.060	0.729 ± 0.102	–

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