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Table 2 A summary of results for system noise with standard deviation 1 and observation noise with standard deviation 0.1

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 295.9 41.7 0.88 238.4 71.6 0.77 262.4 42.1 0.86 110.7 37.2 0.75
ENet1 246.3 119.7 0.67 109.2 67 0.62 84.7 140.6 0.38 20.3 70.4 0.22
ENet2 277.9 130.9 0.68 212.8 130 0.62 169.7 241.5 0.41 65.5 132.5 0.33
G1DBN1 223.7 48 0.82 99.9 46.2 0.68 65.1 83.1 0.44 19.3 72.7 0.21
G1DBN2 268.8 83.4 0.76 188.1 64.5 0.74 134.8 104.4 0.56 46.7 85.7 0.35
(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 39.8 13.2 0.75 23.4 20.8 0.53 31.2 16.5 0.65 5.5 15.9 0.26
ENet1 38.5 153.1 0.2 18.6 113 0.14 12.3 186.6 0.06 3.7 85 0.04
ENet2 - - - - - - - - - - - -
G1DBN1 35.6 88.9 0.29 16.8 91.9 0.15 10.3 121.9 0.08 2.4 87.8 0.03
G1DBN2 - - - - - - - - - - - -
  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.