Machine-Learning Single-Stranded DNA Nanoparticles for Bacterial Analysis.

Journal: ACS applied nano materials
Published Date: Dec 14, 2020

Abstract

A two-dimensional nanoparticle-single-stranded DNA (ssDNA) array has been assembled for the detection of bacterial species using machine-learning (ML) algorithms. Out of 60 unknowns prepared from bacterial lysates, 54 unknowns were predicted correctly. Furthermore, the nanosensor array, supported by ML algorithms, was able to distinguish wild-type from its mutant by a single gene difference. In addition, the nanosensor array was able to distinguish untreated wild-type from those treated with antimicrobial drugs. This work demonstrates the potential of nanoparticle-ssDNA arrays and ML algorithms for the discrimination and identification of complex biological matrixes.

Authors

  • Nidhi Nandu
    Department of Chemistry, University at Albany, State University of New York, Albany, New York 12222, United States.
  • Christopher W Smith
    Department of Chemistry, University at Albany, State University of New York, Albany, New York 12222, United States.
  • Taha Bilal Uyar
    Department of Chemistry, University at Albany, State University of New York, Albany, New York 12222, United States.
  • Yu-Sheng Chen
    Department of Chemistry, University at Albany, State University of New York, Albany, New York 12222, United States.
  • Mahera J Kachwala
    Department of Chemistry, University at Albany, State University of New York, Albany, New York 12222, United States.
  • Muhan He
    Department of Chemistry, University at Albany, State University of New York, Albany, New York 12222, United States.
  • Mehmet V Yigit
    Department of Chemistry and The RNA Institute, University at Albany, State University of New York, Albany, New York 12222, United States.

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