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. 2022 Jul 17;23(14):7877. doi: 10.3390/ijms23147877

Table 2.

Performance metrics of fusion features developed using three machine learning models in 10-fold cross-validation and in independent tests.

Feature Model Dim 10-Fold Cross-Validation Independent Test
ACC MCC Sn Sp F1 auPRC auROC ACC MCC Sn Sp F1 auPRC auROC
SSA
+ UniRep b
SVM c 2021 0.861 0.723 0.875 a 0.848 0.863 0.929 0.927 0.867 0.734 0.859 0.875 0.866 0.954 0.952
LGBM c 0.840 0.680 0.848 0.832 0.841 0.933 0.924 0.859 0.719 0.859 0.859 0.859 0.960 0.958
RF c 0.838 0.676 0.840 0.836 0.838 0.923 0.917 0.867 0.735 0.844 0.891 0.864 0.955 0.954
SSA
+ BiLSTM b
SVM c 3726 0.836 0.672 0.848 0.824 0.838 0.915 0.917 0.883 0.766 0.859 0.906 0.880 0.943 0.947
LGBM c 0.848 0.696 0.859 0.836 0.849 0.927 0.927 0.875 0.751 0.906 0.844 0.879 0.961 0.957
RF c 0.824 0.649 0.832 0.816 0.826 0.906 0.911 0.898 0.797 0.891 0.906 0.898 0.959 0.951
UniRep
+ BiLSTM b
SVM c 5505 0.844 0.688 0.859 0.828 0.846 0.921 0.926 0.891 0.783 0.922 0.859 0.894 0.966 0.962
LGBM c 0.863 0.727 0.871 0.855 0.864 0.932 0.935 0.870 0.737 0.859 0.886 0.887 0.972 0.958
RF c 0.832 0.664 0.844 0.820 0.834 0.932 0.930 0.875 0.750 0.859 0.891 0.873 0.963 0.960
SSA
+ UniRep
+ BiLSTM b
SVM c 5626 0.871 0.742 0.863 0.879 0.870 0.943 0.941 0.891 0.783 0.922 0.859 0.894 0.940 0.943
LGBM c 0.855 0.711 0.844 0.867 0.854 0.945 0.942 0.898 0.797 0.891 0.906 0.898 0.971 0.971
RF c 0.840 0.680 0.848 0.832 0.841 0.926 0.925 0.898 0.799 0.859 0.937 0.894 0.963 0.957

a Values representing the best performance values are in bold and are underlined. b SSA + UniRep: SSA features combined with UniRep features; SSA + BiLSTM: SSA features combined with BiLSTM features; UniRep + BiLSTM: UniRep features combined with BiLSTM features; SSA + UniRep + BiLSTM: all the above features are combined. c SVM: support vector machine; LGBM: light gradient boosting machine; RF: random forest.