Spinal Muscle Atrophy Disease Modelling as Bayesian Network

Manal Helal

doi:10.1088/1742-6596/2128/1/012015

Spinal Muscle Atrophy Disease Modelling as Bayesian Network

Manal Helal

Research output: Contribution to journal › Conference article › peer-review

6 Downloads (Pure)

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 language	English
Article number	012015
Number of pages	15
Journal	Journal of Physics: Conference Series
Volume	2128
Early online date	24 Dec 2021
DOIs	https://doi.org/10.1088/1742-6596/2128/1/012015
Publication status	E-pub ahead of print - 24 Dec 2021
Event	6th International conference on Advanced Technology and Applied Sciences (ICaTAS 2021) - Cairo, Egypt Duration: 12 Oct 2021 → 14 Oct 2021 Conference number: 6 https://iopscience.iop.org/issue/1742-6596/2128/1

Keywords

Probabilistic Graphical Models
Spinal Muscle Atrophy
Disease Computational Modelling

Access to Document

10.1088/1742-6596/2128/1/012015Licence: CC BY

Final_Version
© 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/
Final published version, 1.3 MBLicence: CC BY

Cite this

@article{c725f08aa3de4f86be470aca057a5c9c,

title = "Spinal Muscle Atrophy Disease Modelling as Bayesian Network",

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. ",

keywords = "Probabilistic Graphical Models, Spinal Muscle Atrophy, Disease Computational Modelling",

author = "Manal Helal",

note = "{\textcopyright} 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/; 6th International conference on Advanced Technology and Applied Sciences (ICaTAS 2021), ICaTAS 2021 ; Conference date: 12-10-2021 Through 14-10-2021",

year = "2021",

month = dec,

day = "24",

doi = "10.1088/1742-6596/2128/1/012015",

language = "English",

volume = "2128",

journal = "Journal of Physics: Conference Series",

issn = "1742-6588",

publisher = "IOP Publishing Ltd.",

url = "https://iopscience.iop.org/issue/1742-6596/2128/1",

}

TY - JOUR

T1 - Spinal Muscle Atrophy Disease Modelling as Bayesian Network

AU - Helal, Manal

N1 - Conference code: 6

PY - 2021/12/24

Y1 - 2021/12/24

N2 - 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.

AB - 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.

KW - Probabilistic Graphical Models

KW - Spinal Muscle Atrophy

KW - Disease Computational Modelling

U2 - 10.1088/1742-6596/2128/1/012015

DO - 10.1088/1742-6596/2128/1/012015

M3 - Conference article

SN - 1742-6588

VL - 2128

JO - Journal of Physics: Conference Series

JF - Journal of Physics: Conference Series

M1 - 012015

T2 - 6th International conference on Advanced Technology and Applied Sciences (ICaTAS 2021)

Y2 - 12 October 2021 through 14 October 2021

ER -

Spinal Muscle Atrophy Disease Modelling as Bayesian Network

Abstract

Keywords

Access to Document

Fingerprint

Cite this