Selected article for: "Ct threshold cycle and threshold cycle"
Author: shaoqing wen; Yi Wang
Title: Monitoring and predicting viral dynamics in SARS-CoV-2-infected Patients
  • Document date: 2020_4_17
    • ID: e4aha2i8_3
    Snippet: First of all, we reformat the original sparse matrix drawn from ref [2] (viral loads of 18 Chinese infected patients with many missing data points) into a dense matrix (training set) as follow: We apply a linear regression to the above data format. To build a personal model with few data points, we borrow information from the population. We replicate the data points of the query person N times, where N is the number of population data points. We .....
    Document: First of all, we reformat the original sparse matrix drawn from ref [2] (viral loads of 18 Chinese infected patients with many missing data points) into a dense matrix (training set) as follow: We apply a linear regression to the above data format. To build a personal model with few data points, we borrow information from the population. We replicate the data points of the query person N times, where N is the number of population data points. We then concatenate the N folds of personal data points with the population data points to form a combined dataset. In this way, population data points are relatively down weights and act as a prior. The combined dataset is fed into a linear regression model to predict the response value: Cycle threshold (Ct). Ct values of SARS-CoV-2-specific gene on RT-PCR assay are inversely related to viral RNA copy number.

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