Page 13 - MDC Abstract Book & Guide

Abstract
The human microbiome is highly dynamic on multiple timescales, changing dramatically during development of the gut in childhood, with diet, or due to medical interventions. Understanding and being able to manipulate these dynamics is essential for the rational design of microbiome-based diagnostics and therapeutics. However, analysis of longitudinal microbiome data is hampered by a paucity of tailored and principled computational methods that address inherent challenges of these data including temporally irregular and sparse sampling, experimental noise, and complex dependency structures. I will present several novel Bayesian machine learning methods that we have developed to overcome these challenges, and discuss our applications of these tools for developing microbiome- based therapeutics and diagnostics.
Sven Sewitz Eagle Genomics
Sven Sewitz, Head of BioData Innovation, is an experienced and driven scientist, with a highly interdisciplinary background. He gained his PhD in molecular microbiology from Oxford University, and is trained in molecular and cellular biology, translational biology as well as in bioinformatics and data science.
Radouane Oudrhiri Eagle Genomics
Radouane Oudhriri has extensive experience in leading world-class data science initiatives and software and system developments in different industries from Telecom to Healthcare. He is Lean/Six Sigma Master Black Belt with speciality in high-tech, IT and Software engineering. Radouane is a fellow member of the Royal Statistical Society and active member of the ISO international Committee TC69: Applications of statistical methods.
Presentation Title
Modelling Biology Using Graph Language
Abstract
At Eagle Genomics, we use graph databases to model the biological domain. This enables the scientist to access the relevant information and insight they are looking for in a number of industries, including pharmaceuticals, healthcare and the food industry. With the advances in extracting and analysing biological information in the - omics, and the substantial increase in access to computing power, the focus is now on managing and relating these data. The evolution of biological data and information is qualitative as well as quantitative. We have found that the context and complexity of the data has become increasingly as critical as the quantity, and lends itself to establishing graph based causal inference. Within this talk, we will explore the work we have done in modelling biological data dynamically.