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Anomaly identification during polymerase chain reaction for detecting SARS-cov-2 using artificial intelligence trained from simulated data

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datacite.rightshttp://purl.org/coar/access_right/c_16ecspa
dc.contributor.authorVillarreal-González, Reynaldo
dc.contributor.authorAcosta-Hoyos, Antonio J.
dc.contributor.authorGarzón-Ochoa, Jaime A.
dc.contributor.authorGalán-Freyle, Nataly J.
dc.contributor.authorAmar-Sepúlveda, Paola
dc.contributor.authorPacheco-Londoño, Leonardo C.
dc.date.accessioned2021-12-13T15:11:26Z
dc.date.available2021-12-13T15:11:26Z
dc.date.issued2021
dc.description.abstractReal-time reverse transcription (RT) PCR is the gold standard for detecting Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), owing to its sensitivity and specificity, thereby meeting the demand for the rising number of cases. The scarcity of trained molecular biologists for analyzing PCR results makes data verification a challenge. Artificial intelligence (AI) was designed to ease verification, by detecting atypical profiles in PCR curves caused by contamination or artifacts. Four classes of simulated real-time RT-PCR curves were generated, namely, positive, early, no, and abnormal amplifications. Machine learning (ML) models were generated and tested using small amounts of data from each class. The best model was used for classifying the big data obtained by the Virology Laboratory of Simon Bolivar University from real-time RT-PCR curves for SARS-CoV-2, and the model was retrained and implemented in a software that correlated patient data with test and AI diagnoses. The best strategy for AI included a binary classification model, which was generated from simulated data, where data analyzed by the first model were classified as either positive or negative and abnormal. To differentiate between negative and abnormal, the data were reevaluated using the second model. In the first model, the data required preanalysis through a combination of prepossessing. The early amplification class was eliminated from the models because the numbers of cases in big data was negligible. ML models can be created from simulated data using minimum available information. During analysis, changes or variations can be incorporated by generating simulated data, avoiding the incorporation of large amounts of experimental data encompassing all possible changes. For diagnosing SARS-CoV-2, this type of AI is critical for optimizing PCR tests because it enables rapid diagnosis and reduces false positives. Our method can also be used for other types of molecular analyses.eng
dc.format.mimetypepdfspa
dc.identifier.citationVillarreal-González, R.; Acosta- Hoyos, A.J.; Garzon-Ochoa, J.A.; Galán- Freyle, N.J.; Amar-Sepúlveda, P.; Pacheco- Londoño, L.C. Anomaly Identification during Polymerase Chain Reaction for Detecting SARS-CoV-2 Using Artificial Intelligence Trained from Simulated Data. Molecules 2021, 26, 20. https://dx.doi.org/10.3390/ molecules26010020spa
dc.identifier.doihttps://dx.doi.org/10.3390/molecules26010020
dc.identifier.issn1420-3049
dc.identifier.urihttps://hdl.handle.net/20.500.12442/9229
dc.identifier.urlhttps://www.mdpi.com/journal/molecules
dc.language.isoengeng
dc.publisherMDPIspa
dc.publisherFacultad de Ingenieríasspa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacionaleng
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccessspa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.sourceRevista Moleculeseng
dc.sourceVol. 26, No. 1 (2021)
dc.subjectSARS-CoV-2spa
dc.subjectArtificial intelligenceeng
dc.subjectPolymerase chain reactioneng
dc.subjectCOVID-19eng
dc.subjectSimulated dataeng
dc.titleAnomaly identification during polymerase chain reaction for detecting SARS-cov-2 using artificial intelligence trained from simulated dataeng
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.spaArtículo científicospa
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oaire.versioninfo:eu-repo/semantics/acceptedVersionspa
sb.programaMaestría en Gestión y Emprendimiento Tecnológicospa
sb.sedeSede Barranquillaspa

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