Skip to main content

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.