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Table 1 Performance indices on artificial data of half-lives and expression time-courses with different numbers of time samples (n)

From: StaRTrEK:in silico estimation of RNA half-lives from genome-wide time-course experiments without transcriptional inhibition

  \(\alpha _{opt}\) \(q_{\text {max}}\) \(\rho\) pval \(FDR<0.05\)
\(n=12\) 10 3 0.89 \(<10^{-309}\) \(92.5\%\)
\(n=10\) 10 3 0.89 \(<10^{-309}\) \(94.4\%\)
\(n=8\) 10 4 0.89 \(<10^{-309}\) \(94.2\%\)
\(n=6\) 10 2 0.86 \(1.3\cdot 10^{-277}\) \(95.2\%\)
  1. Number of gene expression profiles (\(m = 1000\)) and noise amplitude (\(C=0.2\)) are kept fixed. Legend—\(\alpha _{opt}\): optimal value of the regularization parameter; \(q_{\text {max}}\): error threshold expressed as percentile of the MSE distribution; \(\rho\): Pearson correlation coefficient; pval: p value; FDR: false discovery rate. For each instance of the noise distribution considered, we found a negligible variability of the quality indices, therefore variance is not reported in the table