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Table 3 Performance indices on artificial data of half-lives and expression time-courses with different numbers of half-lives to be estimated (m)

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 \(FDR<0.05\)
\(m=1000\) 10 2 0.86 \(1.3\cdot 10^{-277}\) \(95.2\%\)
\(m=800\) 10 3 0.82 \(3.0\cdot 10^{-180}\) \(93.4\%\)
\(m=500\) 10 3 0.78 \(2.0\cdot 10^{-94}\) \(91.0\%\)
\(m=200\) 10 6 0.78 \(3.5\cdot 10^{-38}\) \(90.0\%\)
  1. Number of time samples (\(n = 6\)) 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