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] In silico drug combination discovery for personalized cancer therapy
Background Drug combination therapy, which is considered as an alternative to single drug
therapy, can potentially reduce resistance and toxicity, and have synergistic efficacy. As drug …
therapy, can potentially reduce resistance and toxicity, and have synergistic efficacy. As drug …
[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 …
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 …
SNRMPACDC: computational model focused on Siamese network and random matrix projection for anticancer synergistic drug combination prediction
Synergistic drug combinations can improve the therapeutic effect and reduce the drug
dosage to avoid toxicity. In previous years, an in vitro approach was utilized to screen …
dosage to avoid toxicity. In previous years, an in vitro approach was utilized to screen …
[HTML][HTML] In-silico prediction of synergistic anti-cancer drug combinations using multi-omics data
Chemotherapy is a routine treatment approach for early-stage cancers, but the effectiveness
of such treatments is often limited by drug resistance, toxicity, and tumor heterogeneity …
of such treatments is often limited by drug resistance, toxicity, and tumor heterogeneity …
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 …
[HTML][HTML] 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 …