Fig. 3 | Nature Communications

Fig. 3

From: A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information

Fig. 3

DTINet outperforms other state-of-the-art methods for DTI prediction. We performed a ten-fold cross-validation procedure to compare the prediction performance of DTINet to that of four state-of-the-art DTI prediction methods, i.e., HNM, CMF, and the extended versions of BLMNII and NetLapRLS (see Supplementary Note 1). Performance of each method was assessed by both the area under ROC curve (AUROC) and the area under precision-recall curve (AUPRC). a All methods were trained and tested on the original collected data set (see the main text), without removing any homologous protein. b All methods are trained and tested on a modified data set, in which homologous proteins were excluded. A pair of two proteins are said to be homologous if their sequence identity score is above 40%. All results were summarized over 10 trials and expressed as mean ± SD

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