Louka Konstantina, Papadakis Stamatios
Enhancing computational thinking in early childhood education through ScratchJr integration
Heliyon
potential benefits drug combination synergy cancer medicine significant yet risks must carefully managed due possibility increased toxicity although artificial intelligence applications demonstrated notable success predicting drug combination synergy several key challenges persist 1 existing models often predict average synergy values across restricted range testing dosages neglecting crucial dose amounts mechanisms action drugs involved 2 many graphbased models rely static proteinprotein interactions failing adapt dynamic contextdependent networks limitation constrains applicability current methods introduced safer subhypergraph attentionbased graph model addressing issues incorporating complex relationships among biological knowledge networks considering dosing effects subjectspecific networks safer outperformed previous models benchmark independent test set analysis subgraph attention weight lung cancer cell line highlighted jakstat signaling pathway prdm12 znf781 cdc5l implicated lung fibrosis safer presents interpretable framework designed identify drugresponsive signals tailored comprehending dose effects subjectspecific molecular contexts model uniquely captures doselevel drug combination responses capability unlocks previously inaccessible avenues investigation compared earlier models finally safer framework leveraged future inquiries investigate molecular networks uniquely characterize individual patients