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 …
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 …
Network propagation predicts drug synergy in cancers
Combination therapies are commonly used to treat patients with complex diseases that
respond poorly to single-agent therapies. In vitro high-throughput drug screening is a …
respond poorly to single-agent therapies. In vitro high-throughput drug screening is a …
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 …
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 …
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 …
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 …
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 …
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 …
SYNPRED: prediction of drug combination effects in cancer using different synergy metrics and ensemble learning
Background In cancer research, high-throughput screening technologies produce large
amounts of multiomics data from different populations and cell types. However, analysis of …
amounts of multiomics data from different populations and cell types. However, analysis of …