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Table 1 Comparison of the sensitivity and positive predictive value of various evolutionary strategies

From: Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset

 

NoDC/NoMut

NoDC/Mut

DC/NoMut

DC/Mut

Recombination

SENS.

PPV

SENS.

PPV

SENS.

PPV

SENS.

PPV

Link R. (High Rate)

43 ± 4

61 ± 12

61 ± 6

58 ± 8

63 ± 3

84 ± 5

68 ± 4

74 ± 8

Link R. (Low Rate)

18 ± 7

18 ± 8

42 ± 8

22 ± 8

68 ± 4

82 ± 9

68 ± 4

80 ± 6

Parental R. (High Rate)

23 ± 7

26 ± 8

56 ± 7

38 ± 4

48 ± 3

68 ± 10

66 ± 4

69 ± 6

Parental R. (Low Rate)

12 ± 5

12 ± 5

33 ± 6

14 ± 4

61 ± 4

79 ± 6

60 ± 2

63 ± 7

  1. We report the ability of link and parental recombination to explore the search space for high (0.4) and low (0.1) exchange rates. Results for link-recombination with high and low exchange rates are respectively presented in rows 1 and 2. Results for parental-recombination with high and low exchange rates are respectively shown in rows 3 and 4. In the first column (NoDC/NoMut), we consider the results for these various recombination strategies when neither mutation nor Deterministic Crowding were used. We only use mutation or Deterministic Crowding in column 2 (NoDC/Mut) and column 3 (DC/NoMut), respectively. Mutation and Deterministic Crowding are used jointly in column 4 (DC/Mut). In a given cell, the figures correspond to the results obtained across 10 runs performed on different and independent datasets with a size of 300. These results are presented as the mean ± the standard deviation of the sensitivity (SENS.) and positive predictive value (PPV) across the ten runs. For the sake of clarity, they are expressed as percentages.