Literature DB >> 34095773

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

Nidhi Nandu1, Christopher W Smith1, Taha Bilal Uyar1, Yu-Sheng Chen1, Mahera J Kachwala1, Muhan He1, Mehmet V Yigit2.   

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.

Entities:  

Keywords:  DNA; MoS2; bacterial detection; fluorescence; nanographene oxide (nGO); sensor array

Year:  2020        PMID: 34095773      PMCID: PMC8174836          DOI: 10.1021/acsanm.0c03001

Source DB:  PubMed          Journal:  ACS Appl Nano Mater        ISSN: 2574-0970


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Review 8.  Chemometric Methods for Spectroscopy-Based Pharmaceutical Analysis.

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  1 in total

1.  Algorithmically Guided Optical Nanosensor Selector (AGONS): Guiding Data Acquisition, Processing, and Discrimination for Biological Sampling.

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  1 in total

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