Synergistic drug combinations have demonstrated effective therapeutic effects in cancer
treatment. Deep learning methods accelerate identification of novel drug combinations by …

The lack of a gold standard synergy quantification method for chemotherapeutic drug
combinations warrants the consideration of different synergy metrics to develop efficient …

Drug combinations have demonstrated great potential in cancer treatments. They alleviate
drug resistance and improve therapeutic efficacy. The fast-growing number of anti-cancer …

Motivation While drug combination therapies are a well-established concept in cancer
treatment, identifying novel synergistic combinations is challenging due to the size of …

Motivation Drug combination therapies have shown promise in clinical cancer treatments.
However, it is hard to experimentally identify all drug combinations for synergistic interaction …

Drug combination therapy is generally more effective than monotherapy in the field of cancer
treatment. However, screening for effective synergistic combinations from a wide range of …

Combination therapy is a promising strategy for the successful treatment of cancer. The
large number of possible combinations, however, mean that it is laborious and expensive to …

Synergistic drug combination prediction tasks based on the computational models have
been widely studied and applied in the cancer field. However, most of models only consider …

Discovering effective anti-tumor drug combinations is crucial for advancing cancer therapy.
Taking full account of intricate biological interactions is highly important in accurately …

Simple Summary This paper introduces a novel graph neural network (a hierarchical graph
pooling model), SANEpool, to effectively detect core sub-networks of significant genes for …