[HTML][HTML] EDST: a decision stump based ensemble algorithm for synergistic drug combination prediction

J Chen, L Wu, K Liu, Y Xu, S He, X Bo - BMC bioinformatics, 2023 - Springer
Introduction There are countless possibilities for drug combinations, which makes it
expensive and time-consuming to rely solely on clinical trials to determine the effects of each …

Ensemble prediction of synergistic drug combinations incorporating biological, chemical, pharmacological, and network knowledge

P Ding, R Yin, J Luo, CK Kwoh - IEEE journal of biomedical and …, 2018 - ieeexplore.ieee.org
Combinatorial therapy may reduce drug side effects and improve drug efficacy, making
combination therapy a promising strategy to treat complex diseases. However, in the …

Discovering synergistic drug combination from a computational perspective

P Ding, J Luo, C Liang, Q Xiao… - Current Topics in …, 2018 - ingentaconnect.com
Synergistic drug combinations play an important role in the treatment of complex diseases.
The identification of effective drug combination is vital to further reduce the side effects and …

[HTML][HTML] Synergistic drug combinations prediction by integrating pharmacological data

C Zhang, G Yan - Synthetic and systems biotechnology, 2019 - Elsevier
There is compelling evidence that synergistic drug combinations have become promising
strategies for combating complex diseases, and they have evident predominance comparing …

Predicting combinative drug pairs via multiple classifier system with positive samples only

JY Shi, JX Li, KT Mao, JB Cao, P Lei, HM Lu… - Computer methods and …, 2019 - Elsevier
Abstract Background and Objective Due to the synergistic effects of drugs, drug combination
is one of the effective approaches for treating complex diseases. However, the identification …

[HTML][HTML] A machine learning method for drug combination prediction

J Li, XY Tong, LD Zhu, HY Zhang - Frontiers in genetics, 2020 - frontiersin.org
Drug combination is now a hot research topic in the pharmaceutical industry, but experiment-
based methodologies are extremely costly in time and money. Many computational methods …

[HTML][HTML] Predicting anticancer synergistic drug combinations based on multi-task learning

D Chen, X Wang, H Zhu, Y Jiang, Y Li, Q Liu, Q Liu - BMC bioinformatics, 2023 - Springer
Background The discovery of anticancer drug combinations is a crucial work of anticancer
treatment. In recent years, pre-screening drug combinations with synergistic effects in a …

PDC-SGB: Prediction of effective drug combinations using a stochastic gradient boosting algorithm

Q Xu, Y Xiong, H Dai, KM Kumari, Q Xu, HY Ou… - Journal of theoretical …, 2017 - Elsevier
Combinatorial therapy is a promising strategy for combating complex diseases by improving
the efficacy and reducing the side effects. To facilitate the identification of drug combinations …

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 …

[HTML][HTML] PermuteDDS: a permutable feature fusion network for drug-drug synergy prediction

X Zhao, J Xu, Y Shui, M Xu, J Hu, X Liu, K Che… - Journal of …, 2024 - Springer
Motivation Drug combination therapies have shown promise in clinical cancer treatments.
However, it is hard to experimentally identify all drug combinations for synergistic interaction …