MFSynDCP: multi-source feature collaborative interactive learning for drug combination synergy prediction
Y Dong, Y Chang, Y Wang, Q Han, X Wen, Z Yang… - BMC …, 2024 - Springer
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 …
treatment. However, screening for effective synergistic combinations from a wide range of …
PermuteDDS: a permutable feature fusion network for drug-drug synergy prediction
X Zhao, J Xu, Y Shui, M Xu, J Hu, X Liu, K Che… - Journal of …, 2024 - Springer
Motivation Drug combination therapies have shown promise in clinical cancer treatments.
However, it is hard to experimentally identify all drug combinations for synergistic interaction …
However, it is hard to experimentally identify all drug combinations for synergistic interaction …
DualSyn: A dual-level feature interaction method to predict synergistic drug combinations
Drug combination therapy can reduce drug resistance and improve treatment efficacy,
making it an increasingly promising cancer treatment method. Although existing …
making it an increasingly promising cancer treatment method. Although existing …
DEML: drug synergy and interaction prediction using ensemble-based multi-task learning
Z Wang, J Dong, L Wu, C Dai, J Wang, Y Wen, Y Zhang… - Molecules, 2023 - mdpi.com
Synergistic drug combinations have demonstrated effective therapeutic effects in cancer
treatment. Deep learning methods accelerate identification of novel drug combinations by …
treatment. Deep learning methods accelerate identification of novel drug combinations by …
Pisces: A cross-modal contrastive learning approach to synergistic drug combination prediction
Drug combination therapy is a promising solution to many complicated diseases. Since
experimental measurements cannot be scaled to millions of candidate combinations, many …
experimental measurements cannot be scaled to millions of candidate combinations, many …
Predicting drug synergy using a network propagation inspired machine learning framework
Q Jin, X Zhang, D Huo, H Xie, D Zhang… - Briefings in …, 2024 - academic.oup.com
Combination therapy is a promising strategy for cancers, increasing therapeutic options and
reducing drug resistance. Yet, systematic identification of efficacious drug combinations is …
reducing drug resistance. Yet, systematic identification of efficacious drug combinations is …
SynergyX: a multi-modality mutual attention network for interpretable drug synergy prediction
Discovering effective anti-tumor drug combinations is crucial for advancing cancer therapy.
Taking full account of intricate biological interactions is highly important in accurately …
Taking full account of intricate biological interactions is highly important in accurately …
SDDSynergy: Learning Important Molecular Substructures for Explainable Anticancer Drug Synergy Prediction
Drug combination therapies are well-established strategies for the treatment of cancer with
low toxicity and fewer adverse effects. Computational drug synergy prediction approaches …
low toxicity and fewer adverse effects. Computational drug synergy prediction approaches …
HANSynergy: Heterogeneous Graph Attention Network for Drug Synergy Prediction
Drug synergy therapy is a promising strategy for cancer treatment. However, the extensive
variety of available drugs and the time-intensive process of determining effective drug …
variety of available drugs and the time-intensive process of determining effective drug …
Interpreting the mechanism of synergism for drug combinations using attention-based hierarchical graph pooling
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 …
pooling model), SANEpool, to effectively detect core sub-networks of significant genes for …