journal article·2020-12-24Open access

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

ACS Applied Nano Materials· Volume 3· Issue 12

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.
Source: europepmc · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Advanced biosensing and bioanalysis techniques · Biosensors and Analytical Detection · Bacteriophages and microbial interactions

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Chemistry, University at Albany, State University of New York, 1400 Washington Avenue, Albany, New York 12222, United States

The RNA Institute, University at Albany, State University of New York, 1400 Washington Avenue, Albany, New York 12222, United States

Funding

National Institute of Food and Agriculture · National Institute of General Medical Sciences

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