Machine-Learning Single-Stranded DNA Nanoparticles for Bacterial Analysis.
Nidhi NanduChristopher W SmithTaha Bilal UyarYu-Sheng ChenMahera J KachwalaMuhan HeMehmet V YigitPublished in: ACS applied nano materials (2020)
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.
Keyphrases
- wild type
- machine learning
- escherichia coli
- deep learning
- artificial intelligence
- circulating tumor
- high density
- high resolution
- high throughput
- nucleic acid
- big data
- cell free
- single molecule
- binding protein
- gene expression
- mass spectrometry
- multidrug resistant
- circulating tumor cells
- iron oxide
- climate change
- pseudomonas aeruginosa
- transcription factor
- bioinformatics analysis
- genome wide identification