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Table 3 Re-scaling models improves prediction - Normal linker length model

From: Predicting nucleosome positioning using a duration Hidden Markov Model

model update re-scaled total sensitivity (%) FDR(%) update total sensitivity (%) FDR(%)
1st 0 10266 (12) 71 (0.4) 31 (0.4) 0 13272 (24) 59 (0.5) 55 (0.4)
  1 10279 (15) 76 (0.4) 27 (0.4) 1 14803 (25) 53 (0.4) 64 (0.3)
  2 10240 (19) 79 (0.3) 23 (0.3) 2 15383 (23) 51 (0.4) 67 (0.3)
4th 0 10280 (16) 74 (0.3) 28 (0.3) 0 12785 (28) 63 (0.4) 51 (0.4)
  1 10267 (20) 79 (0.4) 24 (0.5) 1 14065 (25) 58 (0.3) 59 (0.3)
  2 10220 (24) 81 (0.4) 20 (0.5) 2 14591 (24) 55 (0.4) 62 (0.3)
  1. Total predictions, sensitivity, and false discovery rate (FDR) are the averages (standard deviations in parentheses) based on 10 repeated simulations. For each simulation a maize-like genomic sequence consisting of 10000 nucleosomes and 10001 linkers were simulated using the re-scaled 1st and 4th order yeast models. Each sequence was scanned using the true models (re-scaled, 1st or 4th order) and the yeast models with an initial uniform linker length distribution on 1,..., 200. The results after 0, 1, 2 updates of linker length distribution are compared.