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This is a computational method paper presenting an algorithm for predicting drug synergy in silico, without experimental validation, clinical outcomes, or comparison against wet-lab ground truth.
This is a computational method development study with in silico validation on existing datasets; it does not report clinical outcomes, prospective validation, or experimental confirmation of predicted synergies.
A narrative mini-review presenting conceptual applications of AI in cancer plasticity research; no empirical data, clinical outcomes, or comparative evidence reported.
This is a benchmarking and methods development study using machine learning on existing drug-screening datasets with no prospective validation, clinical outcomes, or independent test cohort, demonstrating feasibility and interpretability of neural ranking models rather than efficacy.
A computational framework demonstrating proof-of-concept integration of multimodal viral data with forecasting capability, but lacking validation against prospective clinical outcomes or independent external datasets.