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Fig. 2 | Journal of Translational Medicine

Fig. 2

From: MKRMDA: multiple kernel learning-based Kronecker regularized least squares for MiRNA–disease association prediction

Fig. 2

Performance comparisons between MKRMDA and some state-of-the-art disease–miRNA association prediction models (HGIMDA, RLSMDA, HDMP, sWBSMDA, MCMDA and RKNNMDA) in terms of ROC curve and AUC based on local and global LOOCV, respectively. As a result, MKRMDA achieved AUCs of 0.9040 and 0.8446 in the global and local LOOCV, which represents more outstanding prediction performance than all the previous classical models

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