MPFFPSDC: A multi-pooling feature fusion model for predicting synergistic drug combinations-Reference-Cited by-同舟云学术

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MPFFPSDC: A multi-pooling feature fusion model for predicting synergistic drug combinations

Published:2023-09 Issue: Volume:217 Page:1-9
ISSN:1046-2023
Container-title:Methods
language:en
Short-container-title:Methods

Author:

Bao Xin,Sun Jianqiang,Yi Ming,Qiu Jianlong,Chen Xiangyong,Shuai Stella C,Zhao Qi

Funder

Department of Education of Liaoning Province

National Natural Science Foundation of China

Natural Science Foundation of Liaoning Province

Publisher

Elsevier BV

Subject

General Biochemistry, Genetics and Molecular Biology,Molecular Biology

Reference51 articles.

1. GraphCDR: a graph neural network method with contrastive learning for cancer drug response prediction;Liu;Briefings in Bioinformatics,2022

2. Screening for Combination Cancer Therapies With Dynamic Fuzzy Modeling and Multi-Objective Optimization;Spolaor;Frontiers in genetics,2021

3. Combining genomic and network characteristics for extended capability in predicting synergistic drugs for cancer;Sun;Nature Communications,2015

4. Genetic and genomic architecture of the evolution of resistance to antifungal drug combinations;Hill;PLoS genetics,2013

5. A powerful drug combination strategy targeting glutamine addiction for the treatment of human liver cancer;Jin;Elife,2020

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A review on graph neural networks for predicting synergistic drug combinations;Artificial Intelligence Review;2024-02-13

2. DVMPDC: A Deep Learning Model Based on Dual-View Representation and Multi-Strategy Pooling for Predicting Synergistic Drug Combinations;Lecture Notes in Computer Science;2024

3. Graph Neural Networks in Cancer and Oncology Research: Emerging and Future Trends;Cancers;2023-12-15

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