Advertisement

Neural Network Classification Method for Solution of the Problem of Monitoring Theremoval of the Theranostics Nanocomposites from an Organism

  • Olga Sarmanova
  • Sergey Burikov
  • Sergey Dolenko
  • Eva von Haartman
  • Didem Sen Karaman
  • Igor Isaev
  • Kirill Laptinskiy
  • Jessica M. Rosenholm
  • Tatiana Dolenko
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 636)

Abstract

In this study artificial neural networks were used for elaboration of the new method of monitoring of excreted nanocomposites-drug carriers and their components in human urine by their fluorescence spectra. The problem of classification of nanocomposites consisting of fluorescence carbon dots covered by copolymers and ligands of folic acid in urine was solved. A set of different architectures of neural networks and 4 alternative procedures of the selection of significant input features: by cross-correlation, cross-entropy, standard deviation and by analysis of weights of a neural network were used. The best solution of the problem of classification of nanocomposites and their components in urine provides the perceptron with 8 neurons in a single hidden layer, trained on a set of significant input features selected using cross-correlation. The percentage of correct recognition averaged over all five classes, is 72.3%.

Keywords

Artificial neural network Inverse problem Fluorescent spectroscopy Carbon nanocomposite Drug carrier 

Notes

Acknowledgments

The following parts of this study were supported by the following foundations: (i) elaboration of optical visualization of nanocomposites using ANN (O.E.S.,I.V.I.,T.A.D.) have been performed at the expense of the grant of Russian Science Foundation (project no. 17-12-01481); (ii) the test of nanocomposites properties (S.A.B., K.A.L.) were supported by the grant of the Russian Foundation for Basic Research no. 15-29-01290 ofi_m.

References

  1. 1.
    Doane, T.L., Burda, C.: The unique role of nanoparticles in nanomedicine: imaging, drug delivery and therapy. Chem. Soc. Rev. 41(7), 2885–2911 (2012)CrossRefGoogle Scholar
  2. 2.
    Hong, G., Diao, S., Antaris, A.L., Dai, H.: Carbon nanomaterials for biological imaging and nanomedicinal therapy. Chem. Rev. 115(19), 10816–10906 (2015)CrossRefGoogle Scholar
  3. 3.
    Dolenko, T., Burikov, S., Vervald, A., Vlasov, I., Dolenko, S., Laptinskiy, K., Rosenholm, J.M., Shenderova, O.: Optical imaging of fluorescent carbon biomarkers using artificial neural networks. J. Biomed. Opt. 19(11), 117007-1–117007-9 (2014)CrossRefGoogle Scholar
  4. 4.
    Laptinskiy, K., Burikov, S., Dolenko, S., Efitorov, A., Sarmanova, O., Shenderova, O., Vlasov, I., Dolenko, T.: Monitoring of nanodiamonds in human urine using artificial neural networks. Phys. Status Solidi A 213(10), 2614–2622 (2016)CrossRefGoogle Scholar
  5. 5.
    Prabhakar, N., Näreoja, T., von Haartman, E., Karaman, D., Burikov, S., Dolenko, T., Deguchi, T., Mamaeva, V., Hänninen, P., Vlasov, I., Shenderova, O., Rosenholm, J.: Functionalization of graphene oxide nanostructures improves photoluminescence and facilitates their use as optical probes in preclinical imaging. Nanoscale 7, 10410–10420 (2015)CrossRefGoogle Scholar
  6. 6.
    Kim, Y.: Role of folate in colon cancer development and progression. J. Nutr. 133, 3731S–3739S (2003)CrossRefGoogle Scholar
  7. 7.
    Efitorov, A., Burikov, S., Dolenko, T., Laptinskiy, K., Dolenko, S.: Significant feature selection in neural network solution of an inverse problem in spectroscopy. Procedia Comput. Sci. 66, 93–102 (2015)CrossRefGoogle Scholar
  8. 8.
    Gevrey, M., Dimopoulos, I., Lek, S.: Review and comparison of methods to study the contribution of variables in artificial neural network models. Ecol. Model. 160, 249–264 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Olga Sarmanova
    • 1
  • Sergey Burikov
    • 1
    • 2
  • Sergey Dolenko
    • 2
  • Eva von Haartman
    • 3
  • Didem Sen Karaman
    • 3
  • Igor Isaev
    • 2
  • Kirill Laptinskiy
    • 1
    • 2
  • Jessica M. Rosenholm
    • 3
  • Tatiana Dolenko
    • 1
    • 2
  1. 1.Physical DepartmentM.V. Lomonosov Moscow State UniversityMoscowRussia
  2. 2.D.V. Skobeltsyn Institute of Nuclear PhysicsM.V. Lomonosov Moscow State UniversityMoscowRussia
  3. 3.Faculty of Science and EngineeringAbo Akademi UniversityTurkuFinland

Personalised recommendations