Selected article for: "asymptomatic patient and symptomatic patient"
Author: Alejandro Lopez-Rincon; Alberto Tonda; Lucero Mendoza-Maldonado; Eric Claassen; Johan Garssen; Aletta D. Kraneveld
Title: Accurate Identification of SARS-CoV-2 from Viral Genome Sequences using Deep Learning
  • Document date: 2020_3_14
    • ID: c2lljdi7_54
    Snippet: We use TAGCACTCTCCAAGGGTGTTC as forward primer and GCAAAGC-CAAAGCCTCATTA as reverse primer. Then, we run an In silico PCR test 265 using FastPCR 6.7 [42] with default parameters, this yields the results from The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.13.990242 doi: bioRxiv preprint The data collected from the GISAID repository [37], also reports metadata, including the status of p.....
    Document: We use TAGCACTCTCCAAGGGTGTTC as forward primer and GCAAAGC-CAAAGCCTCATTA as reverse primer. Then, we run an In silico PCR test 265 using FastPCR 6.7 [42] with default parameters, this yields the results from The copyright holder for this preprint (which was not peer-reviewed) is the . https://doi.org/10.1101/2020.03.13.990242 doi: bioRxiv preprint The data collected from the GISAID repository [37], also reports metadata, including the status of patients. While most of the metadata is missing, we selected 169 patients for which the status is reported; in the dataset, 52 are annotated as asymptomatic and 117 as hospitalized. We reached to the submitters 280 of some of the sequences, and they reported that hospitalized meant that the patients presented evident symptoms of SARS-CoV-2, and could thus be considered symptomatic. We then applied the previously described methodology to discover specific sequences to separate asymptomatic from symptomatic (hospitalized) patients, and reduced the number of 21-bps sequences to the necessary 285 minimum, using a feature reduction algorithm [36] . The algorithm ultimately returns an optimal set of 32 sequences, of 21 bps each: Simply checking for their presence inside a patient sample makes it possible to separate asymptomatic from symptomatic patients with 94% accuracy.

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