Advertisement

Using the Encoder Embedded Framework of Dimensionality Reduction Based on Multiple Drugs Properties for Drug Recommendation

  • Jun Ma
  • Ruisheng ZhangEmail author
  • Rongjing Hu
  • Yong Mu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10942)

Abstract

After obtaining a large amount of drug information, how to extract the most important features from various high-dimensional attribute datasets for drug recommendation has become an important task in the initial stage of drug repositioning. Dimensionality reduction is a necessary and important task for getting the best features in next step. In this paper, three important attribute data about the drugs (i.e., chemical structures, target proteins and side effects) are selected, and two deep frameworks named as F1 and F2 are used to accomplish the task of dimensionality reduction. The processed data are used for recommending new indications by collaborative filtering algorithm. In order to compare the results, two important values of Mean Absolute Error (MAE) and Coverage are selected to evaluate the performance of the two frameworks. Through the comparison with the results of Principal Components Analysis (PCA), it shows that the two deep frameworks proposed in this paper perform better than PCA and can be used for dimensionality reduction task in the future in drug repositioning.

Keywords

Machine learning Autocoder Encoder Dimensionality reduction PCA Drug recommendation 

Notes

Acknowledgments

This work was supported by the Fundamental Research Funds for the Central Universities (No. lzujbky-2017-195).

References

  1. 1.
    Paul, S.M., Lewis-Hall, F.: Drugs in search of diseases. Sci. Transl. Med. 5(186), 228–235 (2013)Google Scholar
  2. 2.
    Paul, S.M., Mytelka, D.S., Dunwiddie, C.T., Persinger, C.C., Munos, B.H., Lindborg, S.R., Schacht, A.L.: How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Dressnature Rev. Drug Discov. 9(3), 203–214 (2010)Google Scholar
  3. 3.
    Ashburn, T.T., Thor, K.B.: Drug repositioning.: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 3(8), 673–683 (2004)Google Scholar
  4. 4.
    Ghofrani, H.A., Osterloh, I.H., Grimminger, F.: Sildenafil: from angina to erectile dysfunction to pulmonary hypertension and beyond. Nat. Rev. Drug Discov. 5(8), 689–702 (2006)Google Scholar
  5. 5.
  6. 6.
  7. 7.
    Janecek, A., Gansterer, W.N., Demel, M., Ecker, G.: On the relationship between feature selection and classification accuracy. J. Mach. Learn. Res. 4, 90–105 (2008)Google Scholar
  8. 8.
    Schneider, G., So, S.S.: Adaptive Systems in Drug Design. Landes Bioscience, Austin (2002)Google Scholar
  9. 9.
    Reutlinger, M., Schneider, G.: Nonlinear dimensionality reduction and mapping of compound libraries for drug discovery. J. Mol. Graph. Model. 34(2), 108 (2012)Google Scholar
  10. 10.
    Hinton, G.: Where do features come from? Cogn. Sci. 38(6), 1078 (2014)Google Scholar
  11. 11.
    Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining, 1st edn., vol. 18(4), pp. 86–103 (2005)Google Scholar
  12. 12.
    Maaten, L.J.P.V., Postma, E.O., Herik, H.J.V.D.: Dimensionality reduction: a comparative review. J. Mach. Learn. Res. 10(1), 1–22 (2007)Google Scholar
  13. 13.
    Karl Pearson, F.R.S.: LIII. On lines and planes of closest fit to systems of points in space. Philos. Mag. 2(11), 559–572 (1957)Google Scholar
  14. 14.
    Hotelling, H.: Analysis of a complex of statistical variables into principal components. Br. J. Educ. Psychol. 24(6), 417–520 (1932)Google Scholar
  15. 15.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)Google Scholar
  16. 16.
    Demers, D., Cottrell, G.W.: Non-linear dimensionality reduction. In: Advances in Neural Information Processing Systems, pp. 580–587 (1992)Google Scholar
  17. 17.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetzbMATHGoogle Scholar
  18. 18.
    Jolliffe, I.T.: Principal Component Analysis. Springer (2005)Google Scholar
  19. 19.
    Linusson, A., Elofsson, M., Andersson, I.E., Dahlgren, M.K.: Statistical molecular design of balanced compound libraries for QSAR modeling. Curr. Med. Chem. 17(19), 2001–2016 (2010)Google Scholar
  20. 20.
    Bengio, Y.: Learning deep architectures for AI. Found. Trends® Mach. Learn. 2(1), 1–127 (2009)zbMATHGoogle Scholar
  21. 21.
    Zhang, R., Li, J., Lu, J., Hu, R., Yuan, Y., Zhao, Z.: Using deep learning for compound selectivity prediction. Curr. Comput. Aid. Drug. 12, 1 (2016)Google Scholar
  22. 22.
    Wang, F., et al.: Exploring the associations between drug side-effects and therapeutic indications. J. Biomed. Inform. 51, 1568–1577 (2014)Google Scholar
  23. 23.
    Wang, Y., Xiao, J., Suzek, T.O., Zhang, J., Wang, J., Bryant, S.H.: PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 37, W623–W633 (2009)Google Scholar
  24. 24.
    Wishart, D.S., Knox, C., Guo, A.C., Cheng, D., Shrivastava, S., Tzur, D., Gautam, B., Hassanali, M.: DrugBank: a knowledgebase for drugs, drug actions and drug targets. Nucleic Acids Res. 36, D901–D906 (2008)Google Scholar
  25. 25.
    Kuhn, M., Campillos, M., Letunic, I., Jensen, L.J., Bork, P.: A side effect resource to capture phenotypic effects of drugs. Mol. Syst. Biol. 6(343), 2016 (2010)Google Scholar
  26. 26.
    Comparative Toxicogenomics Database. http://ctdbase.org/
  27. 27.
    Gandhi, M., Gandhi, R.T.: Single-pill regimens for HIV-1 infection. N. Engl. J. Med. 371(19), 1845–1846 (2014)Google Scholar
  28. 28.
    Yang, J.: Clinical efficacy of cefoperazone sulbactam combined with uncomplicated gonorrhea in the treatment of uncomplicated gonorrhea. J. Math. Med. 29(8), 1220–02 (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina

Personalised recommendations