  We investigate the molecular gene expressions studies and public databases
for disease modelling using Probabilistic Graphical Models and Bayesian
Inference. A case study on Spinal Muscle Atrophy Genome-Wide Association Study
results is modelled and analyzed. The genes up and down-regulated in two stages
of the disease development are linked to prior knowledge published in the
public domain and co-expressions network is created and analyzed. The Molecular
Pathways triggered by these genes are identified. The Bayesian inference
posteriors distributions are estimated using a variational analytical algorithm
and a Markov chain Monte Carlo sampling algorithm. Assumptions, limitations and
possible future work are concluded.
