Artificial intelligence and machine learning methods in predicting anti-cancer drug combination effects

K Fan, L Cheng, L Li - Briefings in bioinformatics, 2021 - academic.oup.com
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 …

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 …

A granularity-level information fusion strategy on hypergraph transformer for predicting synergistic effects of anticancer drugs

W Wang, G Yuan, S Wan, Z Zheng, D Liu… - Briefings in …, 2024 - academic.oup.com
Combination therapy has exhibited substantial potential compared to monotherapy.
However, due to the explosive growth in the number of cancer drugs, the screening of …

DCE‐DForest: A Deep Forest Model for the Prediction of Anticancer Drug Combination Effects

W Zhang, Z Xue, Z Li, H Yin - Computational and Mathematical …, 2022 - Wiley Online Library
Drug combinations have recently been studied intensively due to their critical role in cancer
treatment. Computational prediction of drug synergy has become a popular alternative …

Jasminoidin and ursodeoxycholic acid exert synergistic effect against cerebral ischemia-reperfusion injury via Dectin-1-induced NF-κB activation pathway

DL Hao, R Xie, YL Zhong, JM Li, QH Zhao, HR Huo… - Phytomedicine, 2023 - Elsevier
Background Jasminoidin (JA) and ursodeoxycholic acid (UA) were shown to act
synergistically against ischemic stroke (IS) in our previous studies. Purpose To investigate …

A novel approach to predicting the synergy of anti-cancer drug combinations using document-based feature extraction

Y Shim, M Lee, PJ Kim, HG Kim - BMC bioinformatics, 2022 - Springer
Background To reduce drug side effects and enhance their therapeutic effect compared with
single drugs, drug combination research, combining two or more drugs, is highly important …

Probing synergistic targets by natural compounds for hepatocellular carcinoma

J Gao, Z Yin, Z Wu, Z Sheng, C Ma, R Chen… - Frontiers in Cell and …, 2021 - frontiersin.org
Background Designing combination drugs for malignant cancers has been restricted due to
the scarcity of synergy-medicated targets, while some natural compounds have …

Predicting anti-cancer drug synergy using extended drug similarity profiles

SR Hosseini, X Zhou - bioRxiv, 2022 - biorxiv.org
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 …

Dual-view jointly learning improves personalized drug synergy prediction

X Li, B Shen, F Feng, K Li, L Ma, H Li - bioRxiv, 2024 - biorxiv.org
Background: Accurate and robust estimation of the synergistic drug combination is important
for precision medicine. Although some computational methods have been developed, some …

Drug synergy model for malignant diseases using deep learning

P Rani, K Dutta, V Kumar - Journal of Bioinformatics and …, 2023 - World Scientific
Drug synergy has emerged as a viable treatment option for malignancy. Drug synergy
reduces toxicity, improves therapeutic efficacy, and overcomes drug resistance when …