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Table 3 A summary of results for system noise with standard deviation 1 and observation noise with standard deviation 1

From: Identifying regulational alterations in gene regulatory networks by state space representation of vector autoregressive models and variational annealing

(a)             
  Equally spaced Unequally spaced
# of time points 50 25 50 25
  # TP # FP PRE # TP # FP PRE # TP # FP PRE # TP # FP PRE
Proposed 190.2 122.8 0.61 88.1 121.0 0.42 132.1 675.5 0.66 52.1 108.9 0.33
ENet1 110.8 136.9 0.45 30.3 75.9 0.29 32.5 133.7 0.2 7.4 75.2 0.09
ENet2 189.8 218 0.47 85.8 136.2 0.39 75.9 180.7 0.3 23.5 123.3 0.16
GIDBN1 86.6 82.6 0.51 22.6 90.8 0.2 26.3 74 0.26 7.1 71.6 0.09
GIDBN2 163.9 105.7 0.61 54.4 99 0.35 66.2 91.2 0.42 17.4 92.8 0.16
(b)             
  Equally spaced Unequally spaced
# of time points 50 25 50 25
  # TP # FP PRE # TP # FP PRE # TP # FP PRE # TP # FP PRE
Proposed 15.4 43.6 0.26 4.7 50.0 0.09 8.1 16.7 0.33 3.1 42.5 0.07
ENet1 16.9 184 0.08 3.8 95.4 0.04 5.2 155 0.03 1.2 81.4 0.01
ENet2 - - - - - - - - - - - -
GIDBN1 14.5 125.1 0.1 3.9 105.7 0.04 4 91.9 0.04 1.7 76.8 0.02
GIDBN2 - - - - - - - - - - - -
  1. (a) The number of true positives (# TP) and false positives (# FP) of estimated regulations in two network model by the proposed approach, ENet1, ENet2, G1DBN1, and G1DBN2 for equally and unequally spaced time series data. PRE denotes the precision of the results. Regulations in two networks are 305 in total. (b) The number of true positives (# TP) and false positives (# FP) of changes on regulations between two network models estimated by the proposed approach, ENet1, ENet2, G1DBN1, and G1DBN2 for equally and unequally spaced time series data. Since no changes are estimated by ENet2 and G1DBN2, their results are indicated by '-'. The regulations changed in two networks are in total 47.