Spinal Muscle Atrophy Disease Modelling as Bayesian Network

This week on Journal Club session Manal Helal will talk about her current work on "Spinal Muscle Atrophy Disease Modelling as Bayesian Network".

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 2 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.


Date: 2021/10/08
Time: 14:00
Location: online

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Associative memory olfaction Deep learning Artificial intelligence Studentship Neurons Evolutionary algorithms PCA cerebellum Neuronal Morphology Purkinje cell Software development computational neuroscience Convolutional Neural Networks Structural plasticity Persistent homology Neuromorphic hardware Computer Science optimization Homoeostasis robotics Dendritic computation computational modelling neural networks machine learning neuroscience synaptic plasticity Network connectivity COVID-19 Open position