Table 1.
Performance comparison of different algorithms based on five indicators.
Fig 1.
ROC curves of ASGCL model and five comparison models.
(a) ROC curves on GDSC dataset (b) ROC curves on CCLE dataset.
Fig 2.
Scatter plot of predicting drug response (IC50 value) in GDSC using ASGCL model.
Fig 3.
Predict the IC50 values of unknown cell line drug reactions grouped by drugs.
Drugs are classified based on the median predicted IC50 values of all missing cell lines, and the top 10 drugs with the highest median IC50 have the worst efficacy; The last 10 drugs with the lowest IC50 median may be the most effective.
Table 2.
Ablation experiments using different contrasting methods.
Fig 4.
Research on ablation of nonlinear subspaces.
Fig 5.
Comparative experiments on different graph encoders in different neighborhoods.
(a) Experimental results of GCN on different neighborhood layers (b) Experimental results of GAT on different neighborhood layers
Fig 6.
on the ASGCL model’s performance is demonstrated, where the blue dotted line represents the baseline result of the ASGCL model on the GDSC dataset when the parameter ρℒ is not introduced.
Fig 7.
The impact of the parameter on ρℒ.
The ASGCL model’s performance is demonstrated, where the blue dotted line represents the baseline result of the ASGCL model on the GDSC dataset when the parameter is not introduced.
Fig 8.
The parameter and ρℒ impact the performance of the ASGCL model on the GDSC dataset.
In the graph, the colors represent the magnitude of the ASGCL’s performance: darker colors correspond to higher AUC values, while lighter colors indicate lower AUC values.
Fig 9.
Schematic diagram of the ASGCL model.
Module A utilizes a nonlinear subspace to extract cell line and drug features as primary characteristics; Module B, named GraphMorpher, adaptively sparsify the input graph structure; Module C is a contrastive learning module, which enhances the model’s discriminative ability by processing and comparing multiple graph structures.
Fig 10.
Schematic diagram of nonlinear subspace module.
Fig 11.
GraphMorpher diagram.