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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