Machine-Learning Single-Stranded DNA Nanoparticles for Bacterial Analysis

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...

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Published inACS applied nano materials Vol. 3; no. 12; pp. 11709 - 11714
Main Authors Nandu, Nidhi, Smith, Christopher W, Uyar, Taha Bilal, Chen, Yu-Sheng, Kachwala, Mahera J, He, Muhan, Yigit, Mehmet V
Format Journal Article
LanguageEnglish
Published American Chemical Society 24.12.2020
Subjects
artificial intelligence
Escherichia coli
genes
mutants
sensors (equipment)
single-stranded DNA
bacterial detection
nanographene oxide (nGO)
MoS2
fluorescence
DNA
sensor array
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ISSN2574-0970
2574-0970
DOI10.1021/acsanm.0c03001

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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.
AbstractList 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.
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.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.
Author Yigit, Mehmet V
Smith, Christopher W
Chen, Yu-Sheng
Kachwala, Mahera J
He, Muhan
Uyar, Taha Bilal
Nandu, Nidhi
AuthorAffiliation Department of Chemistry
The RNA Institute
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Keywords bacterial detection
nanographene oxide (nGO)
MoS2
fluorescence
DNA
sensor array
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Snippet A two-dimensional nanoparticle–single-stranded DNA (ssDNA) array has been assembled for the detection of bacterial species using machine-learning (ML)...
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SubjectTerms artificial intelligence
Escherichia coli
genes
mutants
sensors (equipment)
single-stranded DNA
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Title Machine-Learning Single-Stranded DNA Nanoparticles for Bacterial Analysis
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