Bayesian Networks in the Presence of Temporal Information - Bayesian Networks in R: With Applications in Systems Biology - page 82

Number of changepoint
− target gene: 1 −

Changepoint position
− target gene: 1 −

Target gene expression profile Parent gene expression profiles

1.0

1.0

5 5

0.8

0.8

0 0

0.6

0.6

0.4

0.4

−5 −5

0.2

0.2

−10 −10

0.0

0.0

0 5 10 15 20 25 30 012345

0 5 10 15 20 25 30

0 5 10 15 20 25 30

Changepoint number
( max. value = 1 )

Time point

Time point

Time point
( # of selected changepoint(s) = 1 )

Regulatory model for target gene: 1
Temporal segment # 1 : [ 2 , 10 ]

Regulatory model for target gene: 1
Temporal segment # 2 : [ 11 , 30 ]

Sub−network # 1
( time point 2 to 10 )

Sub−network # 2
( time point 11 to 30 )

1.0 1.0

TF5

TF5

0.8 0.8

TF2

TF2

TF1

TF1

0.6 0.6

1

1

0.4 0.4

TF4 TF4

0.2 0.2

TF3 TF3

0.0 0.0

TF1 TF2 TF3 TF4 TF5 TF1 TF2 TF3 TF4 TF5

Positive interaction
Negative interaction

Positive interaction
Negative interaction Parent genes

# of selected edge(s): 0

Parent genes
# of selected edge(s): 1

Fig. 3.8 Graphical outputs obtained from the ARTIVA package. Results presented here were obtained analyzing the simulated data sets
data(simulatedData) available in the ARTIVA package, applying function ARTIVAsubnet . The TF3 was considered simultaneously as parent and
target, thus allowing the identification of an auto-regulation mechanism