Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects
Drug combinations have exhibited promising therapeutic effects in treating cancer patients
with less toxicity and adverse side effects. However, it is infeasible to experimentally screen …
with less toxicity and adverse side effects. However, it is infeasible to experimentally screen …
DCE‐DForest: A Deep Forest Model for the Prediction of Anticancer Drug Combination Effects
W Zhang, Z Xue, Z Li, H Yin - Computational and Mathematical …, 2022 - Wiley Online Library
Drug combinations have recently been studied intensively due to their critical role in cancer
treatment. Computational prediction of drug synergy has become a popular alternative …
treatment. Computational prediction of drug synergy has become a popular alternative …
[HTML][HTML] Deep learning-based multi-drug synergy prediction model for individually tailored anti-cancer therapies
S She, H Chen, W Ji, M Sun, J Cheng, M Rui… - Frontiers in …, 2022 - frontiersin.org
While synergistic drug combinations are more effective at fighting tumors with complex
pathophysiology, preference compensating mechanisms, and drug resistance, the …
pathophysiology, preference compensating mechanisms, and drug resistance, the …
Predicting synergistic anticancer drug combination based on low-rank global attention mechanism and bilinear predictor
Motivation Drug combination therapy has exhibited remarkable therapeutic efficacy and has
gradually become a promising clinical treatment strategy of complex diseases such as …
gradually become a promising clinical treatment strategy of complex diseases such as …
Prediction of synergistic anti-cancer drug combinations based on drug target network and drug induced gene expression profiles
Objective Synergistic drug combinations are promising therapies for cancer treatment.
However, effective prediction of synergistic drug combinations is quite challenging as …
However, effective prediction of synergistic drug combinations is quite challenging as …
A review of machine learning approaches for drug synergy prediction in cancer
A Torkamannia, Y Omidi… - Briefings in Bioinformatics, 2022 - academic.oup.com
Combinational pharmacotherapy with the synergistic/additive effect is a powerful treatment
strategy for complex diseases such as malignancies. Identifying synergistic combinations …
strategy for complex diseases such as malignancies. Identifying synergistic combinations …
DeepSynergy: predicting anti-cancer drug synergy with Deep Learning
Motivation While drug combination therapies are a well-established concept in cancer
treatment, identifying novel synergistic combinations is challenging due to the size of …
treatment, identifying novel synergistic combinations is challenging due to the size of …
[HTML][HTML] 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 …
AttenSyn: an attention-based deep graph neural network for anticancer synergistic drug combination prediction
Identifying synergistic drug combinations is fundamentally important to treat a variety of
complex diseases while avoiding severe adverse drug–drug interactions. Although several …
complex diseases while avoiding severe adverse drug–drug interactions. Although several …
CCSynergy: an integrative deep-learning framework enabling context-aware prediction of anti-cancer drug synergy
SR Hosseini, X Zhou - Briefings in bioinformatics, 2023 - academic.oup.com
Combination therapy is a promising strategy for confronting the complexity of cancer.
However, experimental exploration of the vast space of potential drug combinations is costly …
However, experimental exploration of the vast space of potential drug combinations is costly …