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基于药物互作网络的协同与拮抗预测研究

刘文斌 陈杰 方刚 石晓龙 许鹏

引用本文: 刘文斌, 陈杰, 方刚, 石晓龙, 许鹏. 基于药物互作网络的协同与拮抗预测研究[J]. 电子与信息学报, doi: 10.11999/JEIT190867 shu
Citation:  Wenbin LIU, Jie CHEN, Gang FANG, Xiaolong SHI, Peng XU. Prediction of Drug Synergy and Antagonism Based on Drug-Drug Interaction Network[J]. Journal of Electronics and Information Technology, doi: 10.11999/JEIT190867 shu

基于药物互作网络的协同与拮抗预测研究

    作者简介: 刘文斌: 男,1969年生,教授,研究方向为生物信息学;
    陈杰: 男,1994年生,硕士生,研究方向为生物信息学;
    方刚: 男,1969年生,教授,研究方向为生物信息学;
    石晓龙: 男,1975年生,教授,研究方向为生物信息学;
    许鹏: 男,1986年生,博士后,研究方向为生物信息学
    通讯作者: 刘文斌,wbliu6910@126.com 许鹏,gdxupeng@gzhu.edu.cn
  • 基金项目: 国家重点研发计划(2019YFA0706402),国家自然科学基金(61572367, 61573017, 61972107, 61972109)

摘要: 药物的协同与拮抗关系预测,有助于药物的使用安全及组合用药的发展。该文从药物互作网络(DDINet)出发,基于网络拓扑结构构造分类特征,提出一种预测药物协同和拮抗关系的方法。从特征选择结果可知,根据药物与其公共邻居节点关系构造的特征表现出了明显的正负样本分布差距,能有效地反映出药物的协同或拮抗关系。在使用不同特征分类器的分类结果中,最优AUC和分类精度值分别达到了0.9687和0.9187。而在协同与拮抗关系预测结果中,其预测精度值达到了0.45和0.75以上。这说明基于网络拓扑结构的方法能有效对药物协同和拮抗关系进行分类和预测。与传统基于药物功能、结构、靶基因等相似性特征的方法相比,本方法计算简单高效,将会有效促进组合用药的发展。

English

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  • 图 1  药物Di和Dj的1阶邻居节点拓扑关系示意

    图 2  特征x1x5在正负样本中的分布

    图 3  特征y1y5, z1在正负样本中的分布

    图 4  特征x3, x4, y2, y3, y4, z1在正负样本中的分布

    图 5  不同特征组合的ROC曲线

    图 6  不同f取值对应的预测样本分布情况

    图 7  不同f, L取值下的协同、拮抗关系预测精度

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