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

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dc.authorid0000-0001-6202-5121en_US
dc.contributor.authorSmith, Christopher W.
dc.contributor.authorHızır, Mustafa Salih
dc.contributor.authorNandu, Nidhi
dc.contributor.authorYigit, Mehmet, V
dc.date.accessioned2022-08-05T06:29:00Z
dc.date.available2022-08-05T06:29:00Z
dc.date.issued2021en_US
dc.departmentBTÜ, Mühendislik ve Doğa Bilimleri Fakültesi, Kimya Bölümüen_US
dc.description.abstractHere, we report a biomarker-free detection of various biological targets through a programmed machine learning algorithm and an automated computational selection process termed algorithmically guided optical nanosensor selector (AGONS). The optical data processed/used by algorithms are obtained through a nanosensor array selected from a library of nanosensors through AGONS. The nanosensors are assembled using two-dimensional nanoparticles (2D-nps) and fluorescently labeled single-stranded DNAs (F-ssDNAs) with random sequences. Both 2D-np and F-ssDNA components are cost-efficient and easy to synthesize, allowing for scaled-up data collection essential for machine learning modeling. The nanosensor library was subjected to various target groups, including proteins, breast cancer cells, and lethal-7 (let-7) miRNA mimics. We have demonstrated that AGONS could select the most essential nanosensors while achieving 100% predictive accuracy in all cases. With this approach, we demonstrate that machine learning can guide the design of nanosensor arrays with greater predictive accuracy while minimizing manpower, material cost, computational resources, instrumentation usage, and time. The biomarker-free detection attribute makes this approach readily available for biological targets without any detectable biomarker. We believe that AGONS can guide optical nanosensor array setups, opening broader opportunities through a biomarker-free detection approach for most challenging biological targets.en_US
dc.identifier.doi10.1021/acs.analchem.1c04379en_US
dc.identifier.endpage1202en_US
dc.identifier.issn0003-2700
dc.identifier.issn1520-6882
dc.identifier.issue2en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1195en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12885/2019
dc.identifier.volume94en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.institutionauthorHızır, Mustafa Salih
dc.language.isoenen_US
dc.publisherAmerican Chemical Societyen_US
dc.relation.ispartofANALYTICAL CHEMISTRYen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectChemistryen_US
dc.titleAlgorithmically Guided Optical Nanosensor Selector (AGONS): Guiding Data Acquisition, Processing, and Discrimination for Biological Samplingen_US
dc.typeArticleen_US

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