Machine learning detects SARS-CoV-2 and variants rapidly on DNA aptamer metasurfaces

Torun, Hulya; Bilgin, Buse; İlgü, Müslüm; Yanik, Cenk; Batur, Numan; Çelik, Süleyman Utku; Ozturk, Meric; Doğan, Özlem; Ergönül, Önder; Solaroğlu, İhsan; Can, Füsun; Onbaşlı, Mehmet C.

doi:10.1101/2021.08.07.21261749

Cited by 5 publications

(2 citation statements)

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“…AI technologies have been applied to diagnose the coronavirus [ 231 ], and there are proposals to integrate them with meta-sensors. Notably, the SARS-CoV-2 saliva sensor showed outstanding sensitivity and specificity, both achieving 95.2%, in clinical trials [ 232 ]. Developing metasurfaces with desired properties and functions relies on innovative design strategies and state-of-the-art computational techniques.…”

Section: Challenges and Potential Directionsmentioning

confidence: 99%

Recent Advances in Metaphotonic Biosensors

2023

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Metaphotonic devices, which enable light manipulation at a subwavelength scale and enhance light–matter interactions, have been emerging as a critical pillar in biosensing. Researchers have been attracted to metaphotonic biosensors, as they solve the limitations of the existing bioanalytical techniques, including the sensitivity, selectivity, and detection limit. Here, we briefly introduce types of metasurfaces utilized in various metaphotonic biomolecular sensing domains such as refractometry, surface-enhanced fluorescence, vibrational spectroscopy, and chiral sensing. Further, we list the prevalent working mechanisms of those metaphotonic bio-detection schemes. Furthermore, we summarize the recent progress in chip integration for metaphotonic biosensing to enable innovative point-of-care devices in healthcare. Finally, we discuss the impediments in metaphotonic biosensing, such as its cost effectiveness and treatment for intricate biospecimens, and present a prospect for potential directions for materializing these device strategies, significantly influencing clinical diagnostics in health and safety.

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Section: Challenges and Potential Directionsmentioning

confidence: 99%

Recent Advances in Metaphotonic Biosensors

2023

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“…At this pandemic stage, keeping the spread of new variants under control becomes a key issue. In this context, inspired by a multitude of applications in bioinformatics [16,15,14,18,7], several methods of variants classification have been proposed exploiting Machine Learning (ML) and Deep Learning (DL) techniques [8,6,22]. These methods provide efficient tools for the classification and clustering of SARS-CoV-2 samples.…”

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confidence: 99%

SARS-CoV-2 variants classification and characterization

Borgato¹,

Bottino²,

Lovino³

et al.

EPiC Series in Computing

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As of late 2019, the SARS-CoV-2 virus has spread globally, giving several variants over time. These variants, unfortunately, differ from the original sequence identified in Wuhan, thus risking compromising the efficacy of the vaccines developed. Some software has been released to recognize currently known and newly spread variants. However, some of these tools are not entirely automatic. Some others, instead, do not return a detailed characterization of all the mutations in the samples. Indeed, such characterization can be helpful for biologists to understand the variability between samples. This paper presents a Machine Learning (ML) approach to identifying existing and new variants completely automatically. In addition, a detailed table showing all the alterations and mutations found in the samples is provided in output to the user. SARS-CoV-2 sequences are obtained from the GISAID database, and a list of features is custom designed (e.g., number of mutations in each gene of the virus) to train the algorithm. The recognition of existing variants is performed through a Random Forest classifier while identifying newly spread variants is accomplished by the DBSCAN algorithm. Both Random Forest and DBSCAN techniques demonstrated high precision on a new variant that arose during the drafting of this paper (used only in the testing phase of the algorithm). Therefore, researchers will significantly benefit from the proposed algorithm and the detailed output with the main alterations of the samples.

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A Comparative Study of Machine Learning Approaches for the Detection of SARS-CoV-2 and its Variants

Vesapogu,

Surampudi

2024

Procedia Computer Science

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Machine learning detects SARS-CoV-2 and variants rapidly on DNA aptamer metasurfaces

Cited by 5 publications

References 43 publications

Recent Advances in Metaphotonic Biosensors

Recent Advances in Metaphotonic Biosensors

SARS-CoV-2 variants classification and characterization

A Comparative Study of Machine Learning Approaches for the Detection of SARS-CoV-2 and its Variants

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