Open AccessMachine-Learning Single-Stranded DNA Nanoparticles for Bacterial Analysis
Author(s) -
Nidhi Nandu,
Christopher W. Smith,
Taha Bilal Uyar,
YuSheng Chen,
Mahera J Kachwala,
Muhan He,
Mehmet V. Yigit
Publication year - 2020
Publication title -
acs applied nano materials
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.227
H-Index - 29
ISSN - 2574-0970
DOI - 10.1021/acsanm.0c03001
Subject(s) - nanosensor , escherichia coli , nanoparticle , dna , mutant , silver nanoparticle , nanotechnology , computational biology , wild type , chemistry , biological system , gene , microbiology and biotechnology , biology , biophysics , materials science , biochemistry
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.