Precise cancer detection via the combination of functionalized SERS surfaces and convolutional neural network with independent inputs

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22310%2F20%3A43921087" target="_blank" >RIV/60461373:22310/20:43921087 - isvavai.cz</a>
Alternative codes found
RIV/60461373:22330/20:43921087 RIV/68407700:21230/20:00338035 RIV/00216208:11110/20:10411409
Result on the web
<a href="https://doi.org/10.1016/j.snb.2020.127660" target="_blank" >https://doi.org/10.1016/j.snb.2020.127660</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.snb.2020.127660" target="_blank" >10.1016/j.snb.2020.127660</a>

Result language
angličtina
Original language name
Precise cancer detection via the combination of functionalized SERS surfaces and convolutional neural network with independent inputs
Original language description
Combining the advanced approaches of surface functionalization and chemistry, plasmonics, surface enhanced Raman spectroscopy (SERS), and machine learning, we propose the advanced route for express and precise recognition of normal and cancer cells. Our interdisciplinary approach uses plasmonic coupling between the specific nanoparticles and underlying periodical plasmonic surface and achieves high SERS enhancement factor. The surface of gold multibranched nanoparticles (AuMs) was functionalized with different chemical groups to achieve partially selective entrapping of biomolecules from cells cultivation media and generate information-rich inputs for machine learning methods and SERS-based cells recognition. Evaluation of convolutional neural networks (CNN) training results, performed with ad hoc feature selection method, suggests that the grafted functional groups provide specificity to proteins, nucleic acids and lipids, responsible for cancer line identification. The dataset of SERS control spectra of normal and cancer cell's metabolites were classified by the trained CNN and perfectly distinguished with 100 % prediction accuracy. © 2020 Elsevier B.V.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20501 - Materials engineering

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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