DeepTraSynergy: drug combinations using multimodal deep learning with transformers
Motivation Screening bioactive compounds in cancer cell lines receive more attention.
Multidisciplinary drugs or drug combinations have a more effective role in treatments and …
Multidisciplinary drugs or drug combinations have a more effective role in treatments and …
DTSyn: a dual-transformer-based neural network to predict synergistic drug combinations
J Hu, J Gao, X Fang, Z Liu, F Wang… - Briefings in …, 2022 - academic.oup.com
Drug combination therapies are superior to monotherapy for cancer treatment in many ways.
Identifying novel drug combinations by screening is challenging for the wet-lab experiments …
Identifying novel drug combinations by screening is challenging for the wet-lab experiments …
DTF: deep tensor factorization for predicting anticancer drug synergy
Motivation Combination therapies have been widely used to treat cancers. However, it is
cost and time consuming to experimentally screen synergistic drug pairs due to the …
cost and time consuming to experimentally screen synergistic drug pairs due to the …
TranSynergy: Mechanism-driven interpretable deep neural network for the synergistic prediction and pathway deconvolution of drug combinations
Q Liu, L Xie - PLoS computational biology, 2021 - journals.plos.org
Drug combinations have demonstrated great potential in cancer treatments. They alleviate
drug resistance and improve therapeutic efficacy. The fast-growing number of anti-cancer …
drug resistance and improve therapeutic efficacy. The fast-growing number of anti-cancer …
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 …
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 …
An enhanced cascade-based deep forest model for drug combination prediction
W Lin, L Wu, Y Zhang, Y Wen, B Yan… - Briefings in …, 2022 - academic.oup.com
Combination therapy has shown an obvious curative effect on complex diseases, whereas
the search space of drug combinations is too large to be validated experimentally even with …
the search space of drug combinations is too large to be validated experimentally even with …
MatchMaker: a deep learning framework for drug synergy prediction
Drug combination therapies have been a viable strategy for the treatment of complex
diseases such as cancer due to increased efficacy and reduced side effects. However …
diseases such as cancer due to increased efficacy and reduced side effects. However …
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 …
GraphSynergy: a network-inspired deep learning model for anticancer drug combination prediction
Objective To develop an end-to-end deep learning framework based on a protein–protein
interaction (PPI) network to make synergistic anticancer drug combination predictions …
interaction (PPI) network to make synergistic anticancer drug combination predictions …