YUE Kejuan, ZOU Beiji, WANG Lei, et al., “Prediction of Drug-Drug Interactions Based on Multi-layer Feature Selection and Data Balance,” Chinese Journal of Electronics, vol. 26, no. 3, pp. 585-590, 2017, doi: 10.1049/cje.2017.04.005
Citation: YUE Kejuan, ZOU Beiji, WANG Lei, et al., “Prediction of Drug-Drug Interactions Based on Multi-layer Feature Selection and Data Balance,” Chinese Journal of Electronics, vol. 26, no. 3, pp. 585-590, 2017, doi: 10.1049/cje.2017.04.005

Prediction of Drug-Drug Interactions Based on Multi-layer Feature Selection and Data Balance

doi: 10.1049/cje.2017.04.005
Funds:  This work is supported by the National Natural Science Foundation of China (No.61573380, No.61672542), and the Scientific Research Fund of Hunan Provincial Education Department (No.13C143).
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  • Corresponding author: ZOU Beiji (corresponding author) received the B.S., M.S., and Ph.D degrees from Zhejiang University in 1982, Qinghua University in 1984 and Hunan University in 2001 respectively. He is currently a Professor and served as the dean at the school of Information Science and Engineering at Central South University. His research interests include computer graphics and image processing. (Email:bjzou@csu.edu.cn)
  • Received Date: 2016-07-08
  • Rev Recd Date: 2016-10-17
  • Publish Date: 2017-05-10
  • Drug-drug interactions (DDIs) occur when two drugs react with each other, which may cause unexpected side effects and even death of the patient. Methods that use adverse event reports to predict unexpected DDIs are limited by two critical yet challenging issues. One is the difficulty of selecting discriminative features from numerous redundant and irrelevant adverse events for modeling. The other is the data imbalance, i.e., the drug pairs causing adverse effects are far less than those not causing adverse effects, which leads to poor accuracy of DDIs detection. We propose a multi-layer feature selection method to select discriminative adverse events and apply an over-sampling technique to make the data balanced. The experimental results show that the validation accuracy of positive DDIs on the Canada Vigilance Adverse Reaction Online Database increases to two times, and 110 DDIs are identified on the drug interactions checker of Drugs.com in USA.
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  • D.D.I. Quinn and R.O. Day, "Drug interactions of clinical importance", Drug Safety, Vol.12, No.6, pp.393-452, 1995.
    P.G. Mvan der Heijden, E.P. van Puijenbroek, S. Buuren, et al., "On the assessment of adverse drug reactions from spontaneous reporting systems:the influence of underreporting on odds ratios", Stat Med, Vol.21, No.14, pp.2027-2044, 2002.
    E.L. Olvey, S. Clauschee and D.C. Malone, "Comparison of critical drug-drug interaction listings:the Department of Veterans Affairs medical system and standard reference compendia", Clin Pharmacol Ther, Vol.87, No.1, pp.48-51, 2010.
    N. Tatonetti, T. Liu and R. Altman, "Predicting drug sideeffects by chemical systems biology", Genome Biol, Vol.10, No.9, pp.238.1-238.4, 2009.
    M. Campillos, M. Kuhn, A.C. Gavin, et al., "Drug target identification using side-effect similarity", Science, Vol.321, No.5886, pp.263-266, 2008.
    J.C. Adams, M.J. Keiser, L. Basuino, et al., "A mapping of drug space from the view point of small molecule metabolism", Plos Comput Biol, Vol.5, No.8, pp.e100047, 2009.
    M.J. Keiser, B.L. Roth, B.N. Armbruster, et al., "Relating protein pharmacology by ligand chemistry", Nat Biotechnol, Vol.25, No.2, pp.197-206, 2007.
    M. Kuhn, C. von Mering, M. Campillos, et al., "STITCH:Interaction networks of chemicals and proteins", Nucleic Acids Res, Vol.36, Suppl.1, pp.684-688, 2008.
    L. Xie, J. Li, L. Xie, et al., "Drug discovery using chemical systems biology:identification of the protein-ligand binding network to explain the side effects of CETP inhibitors", PLoS Comput Biol, Vol.5, No.5, pp.e1000387, 2009.
    S.U. Mertens-Talcott, I. Zadezensky, W.V. De Castro, et al., "Grapefruit-drug interactions:Can interactions with drugs be avoided?", The Journal of Clinical Pharmacology, Vol.46, No.12, pp.1390-1416, 2006.
    P.J. Neuvonen, M. Niemi and J.T. Backman, "Drug interactions with lipid-lowering drugs:Mechanisms and clinical relevance", Clinical Pharmacology Therapeutics, Vol.80, No.6, pp.565-581, 2006.
    D.G. Bailey, J. Malcolm, O. Arnold, et al., "Grapefruit juicedrug interactions", British Journal of Clinical Pharmacology, Vol.46, No.2, pp.101-110, 1998.
    K.Y. Yap, W.L. Tay, W.K. Chui, et al., "Clinically relevant drug interactions between anticancer drugs and psychotropic agents", European Journal of Cancer Care, Vol.20, No.1, pp.6-32, 2011.
    V. Santiago, U. Eugenio, L. Santana, et al., "Detection of drugdrug interactions by modeling interaction profile fingerprints", PloS One, Vol.8, No.3, pp.e58321, 2013.
    A. Bate and S.J. Evans, "Quantitative signal detection using spontaneous ADR reporting", Pharmacoepidemiol Drug Saf, Vol.18, No.6, pp.427-436, 2009.
    W. DuMouchel, "Bayesian data mining in large frequency tables, with an application to the fda spontaneous reporting system", Am Stat, Vol.53, No.3, pp.177-190, 1999.
    A.M. Hochberg and M. Hauben, "Time-to-signal comparison for drug safety data-mining algorithms vs. traditional signaling criteria", Clin Pharmacol Ther, Vol.85, No.6, pp.600-606, 2009.
    W. DuMouchel and D. Pregibon, "Empirical Bayes screening for multi-item associations", ACM SIGKDD International Conference on Knowledge Discovery Data Mining, San Francisco, USA, pp.67-76, 2001.
    G.N. Noren, R. Sundberg, A. Bate, et al., "A statistical methodology for drug-drug interaction surveillance", Stat Med, Vol.27, No.16, pp.3057-3070, 2008.
    R. Harpaz, H.S. Chase and C. Friedman, "Mining multi-item drug adverse effect associations in spontaneous reporting systems", BMC Bioinformatics, Vol.11, Suppl.9, pp.S7-S9, 2010.
    N.P. Tatonetti, G.H. Fernald and R.B. Altman, "A novel signal detection algorithm for identifying hidden drug-drug interactions in adverse event reports", J Am Med Inform Assoc, Vol.19, No.1, pp.79-85, 2012.
    H. Yuan, S. Wang, Y. Li, et al., "Feature selection with data field", Chinese Journal of Electronics, Vol.23, No.4, pp.661-665, 2014.
    S. Zhang, L. Zhang, K. Qiu, et al., "Variable selection in logistic regression model", Chinese Journal of Electronics, Vol.24, No.4, pp.813-817, 2015.
    M.A. Hall, "Correlation-based feature selection for machine learning", Ph.D. thesis, The University of Waikato, New Zealand, 1999.
    Y. Zhai, S.P. Wang, N. Ma, et al., "A data mining method for imbalanced datasets based on one-sided link and distribution density of instances", Acta Electronica Sinica, Vol.42, No.7, pp.1311-1319, 2014. (in Chinese)
    H.B. He and E.A. Garcia, "Learning from imbalanced data", IEEE Transactions on Knowledge and Data Engineering, Vol.21, No.9, pp.1263-1284, 2009.
    N.V. Chawla, K.W. Bowyer, L.O. Hall, et al., "SMOTE:synthetic minority over-sampling technique", Journal of Artificial Intelligence Research, Vol.16, No.1, pp.321-357, 2002.
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