Skip to main navigation Skip to search Skip to main content

Machine-Learning Single-Stranded DNA Nanoparticles for Bacterial Analysis

  • Nidhi Nandu
  • , Christopher W. Smith
  • , Taha Bilal Uyar
  • , Yu Sheng Chen
  • , Mahera J. Kachwala
  • , Muhan He
  • , Mehmet V. Yigit
  • SUNY Albany

Research output: Contribution to journalArticlepeer-review

31 Scopus citations

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 Escherichia coli from its mutant by a single gene difference. In addition, the nanosensor array was able to distinguish untreated wild-type E. coli 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.

Original languageEnglish
Pages (from-to)11709-11714
Number of pages6
JournalACS Applied Nano Materials
Volume3
Issue number12
DOIs
StatePublished - Dec 24 2020

Keywords

  • DNA
  • MoS
  • bacterial detection
  • fluorescence
  • nanographene oxide (nGO)
  • sensor array

Fingerprint

Dive into the research topics of 'Machine-Learning Single-Stranded DNA Nanoparticles for Bacterial Analysis'. Together they form a unique fingerprint.

Cite this