Spinal Muscle Atrophy Disease Modelling as Bayesian Network
Helal, Manal
(2021)
Spinal Muscle Atrophy Disease Modelling as Bayesian Network.
Journal of Physics: Conference Series, 2128: 012015.
ISSN 1742-6588
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.
Item Type | Article |
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Additional information | © 2021 The Author(s). Published under licence by IOP Publishing Ltd at https://doi.org/10.1088/1742-6596/2128/1/012015. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/3.0/ |
Keywords | probabilistic graphical models, spinal muscle atrophy, disease computational modelling, artificial intelligence, computer science applications |
Date Deposited | 15 May 2025 15:22 |
Last Modified | 31 May 2025 00:41 |