Spinal Muscle Atrophy Disease Modelling as Bayesian Network
This work provides a domain-specific computational model for understanding Spinal Muscular Atrophy, but it is incremental as it applies existing Bayesian methods to new genomic data without major methodological breakthroughs.
The authors tackled disease modeling for Spinal Muscular Atrophy by analyzing gene expression data using Bayesian networks, identifying up- and down-regulated genes and their associated molecular pathways across disease stages.
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.