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Predicting Synergistic Drug Combinations Based on Fusion of Cell and Drug Molecular Structures

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成果类型:
期刊论文
作者:
Yan, Shiyu;Yu, Gang;Yang, Jiaoxing;Chen, Lingna
通讯作者:
Yan, SY
作者机构:
[Yan, Shiyu; Chen, Lingna; Yan, SY; Yu, Gang] Univ South China, Comp Sch, Hengyang 421001, Peoples R China.
[Yang, Jiaoxing] Univ South China, Affiliated Hosp 1, Hengyang 421001, Peoples R China.
通讯机构:
[Yan, SY ] U
Univ South China, Comp Sch, Hengyang 421001, Peoples R China.
语种:
英文
关键词:
Deep Learning technique;Synergistic drug combination;Computational model;Feature extraction
期刊:
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES
ISSN:
1913-2751
年:
2025
页码:
1-11
基金类别:
China Scholarship Council (CSC) Program; [[2022]715]; [202008430202]
机构署名:
本校为第一且通讯机构
摘要:
Drug combination therapy has shown improved efficacy and decreased adverse effects, making it a practical approach for conditions like cancer. However, discovering all potential synergistic drug combinations requires extensive experimentation, which can be challenging. Recent research utilizing deep learning techniques has shown promise in reducing the number of experiments and overall workload by predicting synergistic drug combinations. Therefore, developing reliable and effective computational methods for predicting these combinations is essential. This paper proposed a novel method called ...

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