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The code for running each model is divided into individual sub-folders. Two types of model execution can be done: 1) run a pretrained model with specified hyperparameters; 2) run a model from scratch with specified hyperparameters. The former execution can be done by running the run_pretrained.sh script and the latter can be done by running run_model_with_hyp.sh script. Hyperparameter tuning has been performed on the validation set and the best set of hyperparameters for each validation strategy (leave-ccls-out/LCO, leave-drugs-out/LDO, leave-pairs-out /LPO) and each pathway collection (KEGG, PID, Reactome) are provided in sub-folders named best_hyp. All pathway-based models (PathDNN, ConsDeepSignaling, HiDRA, PathDSP) are re-implementations of the original models, with a very small component of code being adaptations (direct usage) of the original code provided by the authors of these pathway-based models. References for such adaptations are included in the comments of the code.
drug response prediction, precision medicine, deep learning, interpretable AI, bioinformatics, signalling pathways
drug response prediction, precision medicine, deep learning, interpretable AI, bioinformatics, signalling pathways
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
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