I'm learning to build Bayesian Network Based Modeling of Gene Regulatory Network of Cancerous Cellular Cells. So far, I have learned to form a Bayesian Network based on nature of genes(i.e Functional Genes and TFs). However, I cannot figure out how to give probability to each node/vertex. Is it possibile to somehow iterpret the centrality score of each vertex an associate the meaning with the probabilistic model in terms of cause and effect?

or can you atleast refer me a relevant research paper?

I believe I would be worth mentioning that I'm fairly new to this.

Here's my data of TFs and Sources.

TF Target Model of Regulation References
ABL1 BCL2 Repression 11753601
AHR FOS Unknown 10478842
APC BIRC5 Unknown 11751382
AR ERBB2 Activation 21613411
AR ERBB2 Unknown 21741601

Following is the sample of my data for network analysis, mostly containing centrality scores some graph properties.

SUID name BetweennessCentrality ClosenessCentrality ClusteringCoefficient Eccentricity EdgeCount Indegree IsSingleNode NeighborhoodConnectivity Outdegree PartnerOfMultiEdgedNodePairs selected SelfLoops shared name Stress
7228 TP53 0.06611272 0.46456693 0.04141865 4 75 50 FALSE 14.40625 25 11 FALSE 0 TP53 39572
6788 AR 0.03605041 0.40410959 0.03164983 4 60 46 FALSE 13.38181818 14 5 FALSE 0 AR 20498
7029 MYC 0.03342125 0.35014837 0.03280053 5 81 74 FALSE 12.93589744 7 3 FALSE 0 MYC 19140

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