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 …
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 …
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 …
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 …
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 …
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 …
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 …
PRODeepSyn: predicting anticancer synergistic drug combinations by embedding cell lines with protein–protein interaction network
X Wang, H Zhu, Y Jiang, Y Li, C Tang… - Briefings in …, 2022 - academic.oup.com
Although drug combinations in cancer treatment appear to be a promising therapeutic
strategy with respect to monotherapy, it is arduous to discover new synergistic drug …
strategy with respect to monotherapy, it is arduous to discover new synergistic drug …