Spinal Muscle Atrophy Disease Modelling as Bayesian Network
Applications
2023-11-30 v1 Artificial Intelligence
Abstract
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.
Cite
@article{arxiv.2311.17521,
title = {Spinal Muscle Atrophy Disease Modelling as Bayesian Network},
author = {Mohammed Ezzat Helal and Manal Ezzat Helal and Sherif Fadel Fahmy},
journal= {arXiv preprint arXiv:2311.17521},
year = {2023}
}