[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 …
[HTML][HTML] Machine learning model for anti-cancer drug combinations: Analysis, prediction, and validation
Drug combination therapy is a highly effective approach for enhancing the therapeutic
efficacy of anti-cancer drugs and overcoming drug resistance. However, the innumerable …
efficacy of anti-cancer drugs and overcoming drug resistance. However, the innumerable …
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 …
[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 …
Explainable machine learning prediction of synergistic drug combinations for precision cancer medicine
Although combination therapy has been a mainstay of cancer treatment for decades, it
remains challenging, both to identify novel effective combinations of drugs and to determine …
remains challenging, both to identify novel effective combinations of drugs and to determine …
Synergistic drug combination prediction by integrating multiomics data in deep learning models
Intrinsic and acquired drug resistance is a major challenge in cancer therapy. Synergistic
drug combinations could help to overcome drug resistance. However, the number of …
drug combinations could help to overcome drug resistance. However, the number of …
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 …
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 …
A genetic algorithm-based ensemble learning framework for drug combination prediction
L Wu, X Ye, Y Zhang, J Gao, Z Lin, B Sui… - Journal of Chemical …, 2023 - ACS Publications
Combination therapy is a promising clinical treatment strategy for cancer and other complex
diseases. Multiple drugs can target multiple proteins and pathways, greatly improving the …
diseases. Multiple drugs can target multiple proteins and pathways, greatly improving the …
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 …