Dubai Bioinformatics Digital Masterclass Thursday 7th July 2022

Location
Online
Dates
Thursday 7 July 2022 (15:00-16:00)
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Join our Masterclass on Machine Learning and Artificial Intelligence Approaches to Clinical and Environmental Microbiology and discover more about our Bioinformatics MSc programme.

 Abstract

Since the advent of high-throughput sequencing and phenotypic assays, the fields of clinical and environmental microbiology have undergone a significant transformation. A large volume of data, combined with powerful analysis and predictive tools, has enabled the extraction of predictive and descriptive insights of public health and clinical significance in recent years. During this talk, we will review the research at the Data-Driven Microbiology lab at the University of Birmingham, Dubai and Edgbaston campuses. In particular, we describe how the application of phylogenetic and phylodynamic methods to whole genome sequencing data of bacterial pathogens provided a better understanding into the epidemiology and evolution of these pathogens on epidemiological time scales and revealed high-resolution links between clinical and non-clinical settings in One Health frameworks. A second part of the presentation discusses the use of machine learning methods to predict complex bacterial features, such as antimicrobial resistance, growth, and horizontal gene transfer, from genomic biomarkers and how to turn these models into data science solutions in microbial precision medicine. Our presentation ends with an overview of the MSc in Bioinformatics program in Dubai and its content, as the first postgraduate bioinformatics course offered by a top-hundred university in Dubai. 

 This event will take place on Zoom and a link will be shared upon booking.

Meet the Experts

Dr. Danesh Moradigaravand

Dr Moradigaravand is a Assistant Professor and Group Leader the Data-Driven Microbiology lab at the University of Birmingham. The lab analyses Next Generation Sequencing (NGS) data of microbial populations and communities using a variety of statistical genetic, phylogenetic, and machine learning methods, focusing on the evolution and epidemiology of bacterial infectious diseases across Gram-negative and Gram-positive bacterial strains. The lab extensively utilizes machine learning approaches to extract insights from genomic and metagenomic data.

 

Find out more about Dr. Danesh Moradigaravand