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Table 3 The robustness of target identification across samples of various sizes

From: On an enhancement of RNA probing data using information theory

  N \(n=100\) \(n=200\) \(n=300\)
\({\mathbb {P}}(s\in \Omega ^*)\) \(2^{9}\) \(0.774 \pm 0.175\) \(0.782 \pm 0.171\) \(0.761 \pm 0.182\)
\(2^{10}\) \(0.768 \pm 0.178\) \(0.742 \pm 0.192\) \(0.751 \pm 0.187\)
\(2^{11}\) \(0.747 \pm 0.189\) \(0.711 \pm 0.206\) \(0.738 \pm 0.194\)
\({\mathbb {P}}(s^*=s)\) \(2^{9}\) \(0.685 \pm 0.216\) \(0.698\pm 0.211\) \(0.724 \pm 0.200\)
\(2^{10}\) \(0.669 \pm 0.222\) \(0.646\pm 0.229\) \(0.706 \pm 0.208\)
\(2^{11}\) \(0.682 \pm 0.217\) \( 0.634 \pm 0.237\) \( 0.695 \pm 0.212\)
\({\mathbb {P}}(s^*=s\mid s\in \Omega ^*)\) \(2^{9}\) 0.885 ± 0.279 0.892 ± 0.270 0.951 ± 0.263
\(2^{10}\) 0.871 ± 0.288 0.871 ± 0.309 0.940 ± 0.277
\(2^{11}\) 0.913 ± 0.290 0.864 ± 0.334 0.942 ± 0.288
  1. We generate 1000 random sequences of length n. For each sequence, we then generate (unrestricted) Boltzmann samples of N structures together with a target structure s. The size N of the samples varies from \(2^9\) to \(2^{11}\), and the maximum level of the ensemble tree is given by \(L=\log _2 N+1\). We compute the probabilities of identifying the target utilizing the ensemble tree. We display mean and standard deviation