Spinal Muscle Atrophy Disease Modelling as Bayesian Network

Research output: Contribution to journalConference articlepeer-review

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.
Original languageEnglish
Article number012015
Number of pages15
JournalJournal of Physics: Conference Series
Volume2128
Early online date24 Dec 2021
DOIs
Publication statusE-pub ahead of print - 24 Dec 2021
Event6th International conference on Advanced Technology and Applied Sciences (ICaTAS 2021) - Cairo, Egypt
Duration: 12 Oct 202114 Oct 2021
Conference number: 6
https://iopscience.iop.org/issue/1742-6596/2128/1

Keywords

  • Probabilistic Graphical Models
  • Spinal Muscle Atrophy
  • Disease Computational Modelling

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