# Spinal Muscle Atrophy Disease Modelling as Bayesian Network

arXiv: https://arxiv.org/abs/2311.17521 · HTML: https://mlsanity.com/paper/2311.17521/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 23 references that are in the map.
- First posted on arXiv 29 Nov 2023.

## Builds on (most cited first, 1 of 1)

- [Large-Scale Local Causal Inference of Gene Regulatory Relationships](https://mlsanity.com/paper/1909.03818/) (2019)

## Abstract (arXiv)

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.