SAFER: sub-hypergraph attention-based neural network for predicting effective responses to dose combinations.

ABSTRACT:

Background

The potential benefits of drug combination synergy in cancer medicine are significant, yet the risks must be carefully managed due to the possibility of increased toxicity. Although artificial intelligence applications have demonstrated notable success in predicting drug combination synergy, several key challenges persist: (1) Existing models often predict average synergy values across a restricted range of testing dosages, neglecting crucial dose amounts and the mechanisms of action of the drugs involved. (2) Many graph-based models rely on static protein-protein interactions, failing to adapt to dynamic and higher-order relationships. These limitations constrain the applicability of current methods.

Results

We introduce SAFER, a Sub-hypergraph Attention-based gr

SUBMITTER: Tang YC 

PROVIDER: S-EPMC11290087 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature