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Table 4 Re-scaling models improves prediction - Gamma linker length mode l

From: Predicting nucleosome positioning using a duration Hidden Markov Model

model

update

re-scaled total

sensitivity (%)

FDR(%)

update

total

sensitivity (%)

FDR(%)

1st

0

8746 (28)

60 (0.7)

31 (0.6)

0

10640 (19)

70 (0.3)

35 (0.3)

 

1

9471 (42)

67 (0.7)

30 (0.6)

1

11513 (14)

60 (0.6)

48 (0.6)

 

2

9787 (38)

68 (0.5)

30 (0.4)

2

11812 (18)

55 (0.4)

53 (0.3)

4th

0

8886 (18)

63 (0.3)

29 (0.4)

0

10461 (25)

73 (0.7)

30 (0.7)

 

1

9533 (26)

70 (0.8)

27 (0.8)

1

11190 (32)

66 (0.7)

41 (0.7)

 

2

9775 (33)

72 (0.8)

27 (0.9)

2

11443 (26)

63 (0.5)

45 (0.5)

  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.