高级搜索

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

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

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, 2020, 42(6): 1420-1427. doi: 10.11999/JEIT190867
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, 2020, 42(6): 1420-1427. doi: 10.11999/JEIT190867
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 & Information Technology, 2020, 42(6): 1420-1427. doi: 10.11999/JEIT190867
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 & Information Technology, 2020, 42(6): 1420-1427. doi: 10.11999/JEIT190867

基于药物互作网络的协同与拮抗预测研究

doi: 10.11999/JEIT190867
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)
详细信息
    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

  • 中图分类号: TP301

Prediction of Drug Synergy and Antagonism Based on Drug-Drug Interaction Network

Funds: The National Key R&D Program of China (2019YFA0706402), The National Natural Science Foundation of China (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,该方法计算简单高效,将会有效促进组合用药的发展。
  • 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

  • VAN ROON E N, FLIKWEERT S, LE COMTE M, et al. Clinical relevance of drug-drug interactions[J]. Drug Safety, 2005, 28(12): 1131–1139. doi: 10.2165/00002018-200528120-00007
    CHOU Tingchao. Theoretical basis, experimental design, and computerized simulation of synergism and antagonism in drug combination studies[J]. Pharmacological Reviews, 2006, 58(3): 621–681. doi: 10.1124/pr.58.3.10
    LEHÁR J, KRUEGER A S, AVERY W, et al. Synergistic drug combinations tend to improve therapeutically relevant selectivity[J]. Nature Biotechnology, 2009, 27(7): 659–666. doi: 10.1038/nbt.1549
    CHAIT R, CRANEY A, and KISHONY R. Antibiotic interactions that select against resistance[J]. Nature, 2007, 446(7136): 668–671. doi: 10.1038/nature05685
    VENKATAKRISHNAN K, VON MOLTKE L L, OBACH R S, et al. Drug metabolism and drug interactions: Application and clinical value of in vitro models[J]. Current Drug Metabolism, 2003, 4(5): 423–459. doi: 10.2174/1389200033489361
    PIRMOHAMED M and ORME M L. Drug Interactions of Clinical Importance[M]. DAVIES D M, FERNER R E, and DE GLANVILLE H. Davies's Textbook of Adverse Drug Reactions. 5th ed. London: Chapman & Hall, 1998: 888–912.
    TAKEDA T, HAO Ming, CHENG Tiejun, et al. Predicting drug-drug interactions through drug structural similarities and interaction networks incorporating pharmacokinetics and pharmacodynamics knowledge[J]. Journal of Cheminformatics, 2017, 9: 16. doi: 10.1186/s13321-017-0200-8
    FOKOUE A, SADOGHI M, HASSANZADEH O, et al. Predicting drug-drug interactions through large-scale similarity-based link prediction[C]. The 13th International Conference European Semantic Web Conference, Heraklion, Greece, 2016: 774–789. doi: 10.1007/978-3-319-34129-3_47.
    VILAR S, HARPAZ R, URIARTE E, et al. Drug—drug interaction through molecular structure similarity analysis[J]. Journal of the American Medical Informatics Association, 2012, 19(6): 1066–1074. doi: 10.1136/amiajnl-2012-000935
    VILAR S, URIARTE E, SANTANA L, et al. Detection of drug-drug interactions by modeling interaction profile fingerprints[J]. PLoS One, 2013, 8(3): e58321. doi: 10.1371/journal.pone.0058321
    KASTRIN A, FERK P, and LESKOŠEK B. Predicting potential drug-drug interactions on topological and semantic similarity features using statistical learning[J]. PLoS One, 2018, 13(5): e0196865. doi: 10.1371/journal.pone.0196865
    CHENG Feixiong and ZHAO Zhongming. Machine learning-based prediction of drug-drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties[J]. Journal of the American Medical Informatics Association, 2014, 21(e2): e278–e286. doi: 10.1136/amiajnl-2013-002512
    RYU J Y, KIM H U, and LEE S Y. Deep learning improves prediction of drug-drug and drug-food interactions[J]. Proceedings of the National Academy of Sciences of the United States of America, 2018, 115(18): E4304–E4311. doi: 10.1073/pnas.1803294115
    LUO Heng, ZHANG Ping, HUANG Hui, et al. DDI-CPI, a server that predicts drug-drug interactions through implementing the chemical-protein interactome[J]. Nucleic Acids Research, 2014, 42(W1): W46–W52. doi: 10.1093/nar/gku433
    ZHANG Ping, WANG Fei, HU Jianying, et al. Label propagation prediction of drug-drug interactions based on clinical side effects[J]. Scientific Reports, 2015, 5: 12339. doi: 10.1038/srep12339
    LIU Lili, CHEN Lei, ZHANG Yuhang, et al. Analysis and prediction of drug-drug interaction by minimum redundancy maximum relevance and incremental feature selection[J]. Journal of Biomolecular Structure and Dynamics, 2017, 35(2): 312–329. doi: 10.1080/07391102.2016.1138142
    TAKARABE M, SHIGEMIZU D, KOTERA M, et al. Network-based analysis and characterization of adverse drug-drug interactions[J]. Journal of Chemical Information and Modeling, 2011, 51(11): 2977–2985. doi: 10.1021/ci200367w
    GOTTLIEB A, STEIN G Y, ORON Y, et al. INDI: A computational framework for inferring drug interactions and their associated recommendations[J]. Molecular Systems Biology, 2012, 8: 592. doi: 10.1038/msb.2012.26
    WISHART D S, FEUNANG Y D, GUO A C, et al. DrugBank 5.0: A major update to the DrugBank database for 2018[J]. Nucleic Acids Research, 2017, 46(D1): D1074–D1082. doi: 10.1093/nar/gkx1037
    CORTES C and VAPNIK V. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273–297. doi: 10.1007/bf00994018
    陈素根, 吴小俊. 基于特征值分解的中心支持向量机算法[J]. 电子与信息学报, 2016, 38(3): 557–564. doi: 10.11999/JEIT150693

    CHEN Sugen and WU Xiaojun. Eigenvalue proximal support vector machine algorithm based on eigenvalue decoposition[J]. Journal of Electronics &Information Technology, 2016, 38(3): 557–564. doi: 10.11999/JEIT150693
    汪廷华, 田盛丰, 黄厚宽. 特征加权支持向量机[J]. 电子与信息学报, 2009, 31(3): 514–518. doi: 10.3724/SP.J.1146.2007.01711

    WANG Tinghua, TIAN Shengfeng, and HUANG Houkuan. Feature weighted support vector machine[J]. Journal of Electronics &Information Technology, 2009, 31(3): 514–518. doi: 10.3724/SP.J.1146.2007.01711
    WU Shaomin and FLACH P. A scored AUC metric for classifier evaluation and selection[C]. ICML 2005 Workshop on ROC Analysis in Machine Learning, Bonn, Germany, 2005.
    HOSSIN M and SULAIMAN M N. A review on evaluation metrics for data classification evaluations[J]. International Journal of Data Mining & Knowledge Management Process, 2015, 5(2): 1–11. doi: 10.5121/ijdkp.2015.5201
    ARLOT S and CELISSE A. A survey of cross-validation procedures for model selection[J]. Statistics Surveys, 2010, 4: 40–79. doi: 10.1214/09-SS054
    LIU Weiping and LÜ Linyuan. Link prediction based on local random walk[J]. EPL (Europhysics Letters) , 2010, 89(5): 58007. doi: 10.1209/0295-5075/89/58007
    TANG Jiliang, ALELYANI S, and LIU Huan. Feature Selection for Classification: A Review[M]. AGGARWAL C C. Data Classification: Algorithms and Applications. New York: CRC Press, 2014: 37.
  • 加载中
图(7)
计量
  • 文章访问数:  2603
  • HTML全文浏览量:  1335
  • PDF下载量:  100
  • 被引次数: 0
出版历程
  • 收稿日期:  2019-11-01
  • 修回日期:  2020-01-15
  • 网络出版日期:  2020-02-18
  • 刊出日期:  2020-06-22

目录

    /

    返回文章
    返回