CAN A MACHINE LEARNING ALGORITHM IDENTIFY SARS-COV-2 VARIANTS BASED ON CONVENTIONAL rRT-PCR? PROOF OF CONCEPT
cabrera Alvargonzalez, j.; Larranaga Janeiro, A.; Perez, S.; Martinez Torres, J.; martinez lamas, L.; Davina Nunez, C.; Del Campo Perez, V.; Suarez Luque, S.; Regueiro, B. J.; Porteiro Fresco, J.
Show abstract
1Severe 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.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Detecting SARS-CoV-2 lineages and mutational load in municipal wastewater; a use-case in the metropolitan area of Thessaloniki, Greece 95%
- Design of Specific Primer Set for Detection of B.1.1.7 SARS-CoV-2 Variant using Deep Learning 94%
- Development and Clinical Validation of Swaasa AI Platform for screening and prioritization of Pulmonary TB 94%
Similar papers in this journal
- Error Rates in SARS-CoV-2 Testing Examined with Bayesian Inference 95%
- Modeling of leptospirosis outbreaks in relation to hydroclimatic variables in the northeast of Argentina 93%
- Retrospective and prospective studies evaluating the performance of the SARS-Cov-2 “AQ+ COVID-19 Ag Rapid Test” from InTec on symptomatic and non-symptomatic patients 91%
Similar papers in this journal
- Enhanced Formulation of Precision Probiotics through Active Machine Learning 93%
- Convolutional-LSTM Approach for Temporal Catch Hotspots (CATCH): An AI-Driven Model for Spatiotemporal Forecasting of Fisheries Catch Probability Densities 92%
- Prediction of high-risk liver cancer patients from their mutation profile: Benchmarking of mutation calling techniques 92%
Similar papers in this journal
- Enrichment analysis on regulatory subspaces: a novel direction for the superior description of cellular responses to SARS-CoV-2 95%
- Unsupervised Discovery of Risk Profiles on Negative and Positive COVID-19 Hospitalized Patients 95%
- A machine-learning Approach for Stress Detection Using Wearable Sensors in Free-living Environments 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.