用于细菌分析的机器学习单链DNA纳米颗粒
Machine-Learning Single-Stranded DNA Nanoparticles for Bacterial Analysis.
作者信息
Nandu Nidhi, Smith Christopher W, Uyar Taha Bilal, Chen Yu-Sheng, Kachwala Mahera J, He Muhan, Yigit Mehmet V
机构信息
Department of Chemistry, University at Albany, State University of New York, Albany, New York 12222, United States.
Department of Chemistry and The RNA Institute, University at Albany, State University of New York, Albany, New York 12222, United States.
出版信息
ACS Appl Nano Mater. 2020 Dec 24;3(12):11709-11714. doi: 10.1021/acsanm.0c03001. Epub 2020 Dec 14.
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 from its mutant by a single gene difference. In addition, the nanosensor array was able to distinguish untreated wild-type 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.
一种二维纳米颗粒-单链DNA（ssDNA）阵列已被组装用于使用机器学习（ML）算法检测细菌种类。在从细菌裂解物制备的60个未知样本中，54个未知样本被正确预测。此外，由ML算法支持的纳米传感器阵列能够通过单个基因差异区分野生型及其突变体。此外，纳米传感器阵列能够区分未处理的野生型和用抗菌药物处理过的野生型。这项工作证明了纳米颗粒-ssDNA阵列和ML算法在区分和识别复杂生物基质方面的潜力。
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