Table 1.
Drugs, targets and interactions in each dataset used for validation.
Fig 1.
Converge plot of the MGRNNM algorithm for NR dataset with cross validation setting CVS1 (drug-target pair prediction).
Table 2.
AUPR results for interaction prediction under validation setting CVS1.
Table 3.
AUC results for interaction prediction under validation setting CVS1.
Table 4.
AUPR results for interaction prediction under validation setting CVS2.
Table 5.
AUC results for interaction prediction under validation setting CVS2.
Table 6.
AUPR results for interaction prediction under validation setting CVS3.
Table 7.
AUC results for interaction prediction under validation setting CVS3.
Fig 2.
Three-dimensional mesh depicting the variation of AUPR with the parameters μ1 and μ2 for drug-target interaction prediction using MGRNNM.
Fig 3.
Bar plots depicting that incorporating all the similarities for drugs and targets for prediction task yields best results for every dataset (a) E (b) IC (c) GPCR and (d) NR under the three cross-validation settings in comparison to the cases where each type of similarity was considered separately.
Here, “standard” represents the case when only the chemical structure similarity for drugs and genomic sequence similarity for targets were taken into account and “COMBINED” refers to the use case where all the similarity matrices (standard similarity, Cosine similarity, Correlation, Hamming similarity and Jaccard similarity) were considered.