CAN A MACHINE LEARNING ALGORITHM IDENTIFY SARS-COV-2 VARIANTS BASED ON CONVENTIONAL rRT-PCR? PROOF OF CONCEPT
CAN A MACHINE LEARNING ALGORITHM IDENTIFY SARS-COV-2 VARIANTS BASED ON CONVENTIONAL rRT-PCR? PROOF OF CONCEPT
1 ABSTRACT Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been and remains one of the major challenges humanity has faced thus far. Over the past few months, large amounts of information have been collected that are only now beginning to be assimilated. In the present work, the existence of residual information in the massive numbers of rRT-PCRs that tested positive out of the almost half a million tests that were performed during the pandemic is investigated. This residual information is believed to be highly related to a pattern in the number of cycles that are necessary to detect positive samples as such. Thus, a database of more than 20,000 positive samples was collected, and two supervised classification algorithms (a support vector machine and a neural network) were trained to temporally locate each sample based solely and exclusively on the number of cycles determined in the rRT-PCR of each individual. Finally, the results obtained from the classification show how the appearance of each wave is coincident with the surge of each of the variants present in the region of Galicia (Spain) during the development of the SARS-CoV-2 pandemic and clearly identified with the classification algorithm.
Mart¨anez Lamas Luc¨aa、Garc¨aa Benito Regueiro、P¨|rez Castro Sonia、Davi?a Nu?ez Carlos、Del Campo-P¨|rez V¨actor、Suarez Luque Silvia、Porteiro Fresco Jacobo、Larra?aga Janeiro Ana、Mart¨anez Torres Javier、Cabrera Alvargonz¨￠lez Jorge
Microbiology and Infectology Research Group, Galicia sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO||Microbiology Department, Complexo Hospitalario Universitario de Vigo (CHUVI)Microbiology and Infectology Research Group, Galicia sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO||Microbiology Department, Complexo Hospitalario Universitario de Vigo (CHUVI)||Direcci¨?n Xeral de Sa¨2de P¨2blica, Conseller¨aa de Sanidade, Xunta de Galicia, Santiago de CompostelaMicrobiology and Infectology Research Group, Galicia sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO||Microbiology Department, Complexo Hospitalario Universitario de Vigo (CHUVI)||Universidade de VigoMicrobiology and Infectology Research Group, Galicia sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGODepartment of Preventive Medicine and Public Health, ¨￠lvaro Cunqueiro HospitalDirecci¨?n Xeral de Sa¨2de P¨2blica, Conseller¨aa de Sanidade, Xunta de Galicia, Santiago de CompostelaCintecx, Universidade de Vigo, GTECintecx, Universidade de Vigo, GTEApplied Mathematics I, Telecommunications Engineering School, Universidad de VigoMicrobiology and Infectology Research Group, Galicia sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO||Microbiology Department, Complexo Hospitalario Universitario de Vigo (CHUVI)||Universidade de Vigo
医学研究方法生物科学研究方法、生物科学研究技术
Mart¨anez Lamas Luc¨aa,Garc¨aa Benito Regueiro,P¨|rez Castro Sonia,Davi?a Nu?ez Carlos,Del Campo-P¨|rez V¨actor,Suarez Luque Silvia,Porteiro Fresco Jacobo,Larra?aga Janeiro Ana,Mart¨anez Torres Javier,Cabrera Alvargonz¨￠lez Jorge.CAN A MACHINE LEARNING ALGORITHM IDENTIFY SARS-COV-2 VARIANTS BASED ON CONVENTIONAL rRT-PCR? PROOF OF CONCEPT[EB/OL].(2025-03-28)[2025-05-06].https://www.medrxiv.org/content/10.1101/2021.11.12.21266286.点此复制
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