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SELECTBIO Conferences Metabomeeting 2014

David Broadhurst's Biography

David Broadhurst, Assistant Professor, University of Alberta

Dr. David Broadhurst, born Chester UK, has a BEng (1st Class Hons) in Electronic Engineering, an MSc in Medical Informatics, and a PhD in the ‘‘Application of Artificial Neural Networks and Evolutionary Algorithms to Metabolic Profiling’’. He has been an active member of the metabolomics community for the last 18 years, where he is a recognised expert in Design of Experiments, Signal Processing, Statistics, Machine Learning, Data Visualization, and Bioinformatics. David worked for an extended period as a post-doctoral research fellow at the University of Manchester as part of Prof. Douglas Kell’s Bioanalytical Sciences Group. Here the focus of his applied research shifted from the modelling of microbial/botanical systems, performed during his PhD at the University of Aberystwyth, toward more clinical based research. In particular he helped advance the use of untargeted metabolic profiling in understanding human pathology, with a focus on GC-MS and LC-MS platforms. This included the development and validation of bio-analytical methodologies, the development of strict biobanking and experimental design protocols, and promoting the need for extremely rigorous statistical analysis. Whilst working on the HUSERMET project ( - a 4 year BBSRC, MRC and DTI funded collaborative project between the University of Manchester, AstraZenica, and GSK - David collaborated with Dr. Warrick Dunn, Dr. Andrew Nicholls, and Professor Ian Wilson to develop rigorous protocols for Quality Control and Quality Assurance in large scale Metabolomic studies, with the aim of raising the standards of academic clinical metabolomics to those expected by the pharmaceutical industry.
In 2009 David moved to the Department of Obstetrics and Gynaecology, University College Cork, where, in collaboration with Prof. Louise Kenny, he investigated metabolite biomarkers for two major pregnancy diseases: Preeclampsia and Fetal Growth Restriction. This involved the metabolic profiling of a cohort over 2,000 women on several complementary analytical platforms. Prof. Kenny is leading a European consortium toward the translation of this research into a viable clinical test at the new Irish Centre for Fetal and Neonatal Translational Research ( David continues to collaborate with Prof. Kenny and several other members of this centre.
In January 2011 David took up his current position as Assistant Professor of Biostatistics in the Department of Medicine, University of Alberta, Canada, where he is scientific lead and academic coordinator for a range of clinical metabolomics and systems biology projects within the Faculty of Medicine. As a statistician and engineer David has a great interest in computational method development, with particular focus at the intersection between the fields of epidemiology, machine learning, systems biology and translational medicine. His specific area of applied research is the development and validation of novel molecular based biomarker models for disease diagnosis/prognosis, with the aim of interpreting the complex biological processes from which diseases arise, and progress, at a personalized level. His research involves integrating data from a wide range of complementary Omic platforms (metabolomics, transcriptomics, miRNA, GWAS, deep sequencing etc.). By profiling an individual’s unique set of conditions, needs and circumstances into a bespoke computational algorithm he aims to develop practical methodologies to aid translation of basic systems biology research into viable clinical diagnostic tools.
In Sept 2013 I was appointed to the Faculty of Science (UoA) as an Adjunct Professor in the Department of Mathematical and Statistical Sciences.

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Better Data By Design: Ten Sources of Unwanted Bias and Variance in Metabolomic Experiments

Friday, 12 September 2014 at 10:05

Add to Calendar ▼2014-09-12 10:05:002014-09-12 11:05:00Europe/LondonBetter Data By Design: Ten Sources of Unwanted Bias and Variance in Metabolomic ExperimentsMetabomeeting 2014 in London, UKLondon,

As a general rule, little forethought is given toward the design of metabolomics experiments. Here I categorize the major obstacles against rigorous metabolomic research into a set of 10 sources of unwanted statistical error arising throughout the metabolomics workflow.

Agenda is not currently available
Add to Calendar ▼2014-09-10 00:00:002014-09-12 00:00:00Europe/LondonMetabomeeting 2014Metabomeeting 2014 in London, UKLondon,