NLLSS: predicting synergistic drug combinations based on semi-supervised learning
Fungal infection has become one of the leading causes of hospital-acquired infections with
high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases …
high mortality rates. Furthermore, drug resistance is common for fungus-causing diseases …
Ensemble prediction of synergistic drug combinations incorporating biological, chemical, pharmacological, and network knowledge
Combinatorial therapy may reduce drug side effects and improve drug efficacy, making
combination therapy a promising strategy to treat complex diseases. However, in the …
combination therapy a promising strategy to treat complex diseases. However, in the …
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 …
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 …
Predicting drug-drug interactions based on integrated similarity and semi-supervised learning
C Yan, G Duan, Y Zhang, FX Wu… - … /ACM transactions on …, 2020 - ieeexplore.ieee.org
A drug-drug interaction (DDI) is defined as an association between two drugs where the
pharmacological effects of a drug are influenced by another drug. Positive DDIs can usually …
pharmacological effects of a drug are influenced by another drug. Positive DDIs can usually …
Neighborhood regularized logistic matrix factorization for drug-target interaction prediction
In pharmaceutical sciences, a crucial step of the drug discovery process is the identification
of drug-target interactions. However, only a small portion of the drug-target interactions have …
of drug-target interactions. However, only a small portion of the drug-target interactions have …
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 …
Machine learning methods, databases and tools for drug combination prediction
L Wu, Y Wen, D Leng, Q Zhang, C Dai… - Briefings in …, 2022 - academic.oup.com
Combination therapy has shown an obvious efficacy on complex diseases and can greatly
reduce the development of drug resistance. However, even with high-throughput screens …
reduce the development of drug resistance. However, even with high-throughput screens …
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 …