Synergistic drug combinations prediction by integrating pharmacological Data
Synergistic drug combinations prediction by integrating pharmacological Data
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通过整合药理学数据预测协同药物组合
DOI：
10.1016/j.synbio.2018.10.002
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发表时间：
2019-02
影响因子：
4.8
通讯作者：
Yan Guiying
中科院分区：
文献类型：
--
作者：
Zhang Chengzhi;Yan Guiying
有令人信服的证据表明，协同药物组合已经成为对抗复杂疾病的有希望的策略，并且与传统的一种药物-一种疾病方法相比，它们具有明显的优势。在本文中，我们开发了一种计算方法，即SyFFM，考虑到药理学数据，并应用场感知因子分解机来分析和预测潜在的协同药物组合。首先，基于药物与靶标、酶和指示区域之间的关联构造药物对特征。然后，通过在这些特征的潜在向量空间上实现场感知因子分解机器来获得药物组合的协同得分。最后，可以通过引入阈值来预测协同组合。我们应用SyFFM预测两药协同组合和三药协同组合，交叉验证性能良好。此外，超过90%的预测组合被文献证实，模型中的参数分析表明，我们的方法可以帮助研究和解释组合治疗的协同机制。
There is compelling evidence that synergistic drug combinations have become promising strategies for combating complex diseases, and they have evident predominance comparing to traditional one drug - one disease approaches. In this paper, we develop a computational method, namely SyFFM, that takes pharmacological data into consideration and applies field-aware factorization machines to analyze and predict potential synergistic drug combinations. Firstly, features of drug pairs are constructed based on associations between drugs and target, and enzymes, and indication areas. Then, the synergistic scores of drug combinations are obtained by implementing field-aware factorization machines on latent vector space of these features. Finally, synergistic combinations can be predicted by introducing a threshold. We applied SyFFM to predict pairwise synergistic combinations and three-drug synergistic combinations, and the performance is good in terms of cross-validation. Besides, more than 90% combinations of the top ranked predictions are proved by literature and the analysis of parameters in model shows that our method can help to investigate and explain synergistic mechanisms underlying combinatorial therapy.
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影响因子：
--
作者：
Sun Y;Xiong Y;Xu Q;Wei D
通讯作者：
Wei D
通过化学相互作用、蛋白质相互作用和 KEGG 通路靶点富集预测有效药物组合
影响因子：
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作者：
Chen L;Li BQ;Zheng MY;Zhang J;Feng KY;Cai YD
通讯作者：
Cai YD
影响因子：
120.1
作者：
J. Jia;F. Zhu;Xiaohua Ma;Zhiwei Cao;Yixue Li;Y. Chen
通讯作者：
J. Jia;F. Zhu;Xiaohua Ma;Zhiwei Cao;Yixue Li;Y. Chen
ASDCD：抗真菌协同药物组合数据库
影响因子：
3.7
作者：
Chen X;Ren B;Chen M;Liu MX;Ren W;Wang QX;Zhang LX;Yan GY
通讯作者：
Yan GY
DOI：
10.1109/biocas.2013.6679718
发表时间：
2013-12
期刊：
2013 IEEE Biomedical Circuits and Systems Conference (BioCAS)
影响因子：
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作者：
B. Ligeti;Roberto Vera;Gergely Lukács;Balázs Győrffy;S. Pongor
通讯作者：
B. Ligeti;Roberto Vera;Gergely Lukács;Balázs Győrffy;S. Pongor