Biomedical research: a world of statistical challenges
- Prof. David Gomez-Cabrero, Biological, Environmental Science and Eng, KAUST
B9 L2 H2
A personal presentation describing current “data analysis / statistical” challenges in state-of-the art biomedical projects. First, I will provide an overview of the transition between a PhD in Mathematics to a postdoc in Computational Biology: How did it happen? What were the challenges? Secondly, I will briefly present several current case-studies where statistics and machine learning are core to understand novel biological data related to multi-omic data analysis, spatial profiling, gene therapy and more.
Overview
Abstract
A personal presentation describing current “data analysis / statistical” challenges in state-of-the art biomedical projects. First, I will provide an overview of the transition between a PhD in Mathematics to a postdoc in Computational Biology: How did it happen? What were the challenges? Secondly, I will briefly present several current case-studies where statistics and machine learning are core to understand novel biological data related to multi-omic data analysis, spatial profiling, gene therapy and more.
Brief Biography
David Gomez-Cabrero is an Associate Professor in BESE since early 2021. PhD in Mathematics/Operations Research 2009; MSc Statistics 2003; MSc Operations Research,2004. After completing a Ph.D. in Operations Research, DGC moved to Karolinska Institutet as Postdoctoral researcher; after one year, DGC became a project leader and during 2014 became Assistant Professor. During that time, DGC was Work-Package leader in several European FP7/H2020 projects (Synergy-COPD, STATegra, Frailomic, MultipleMS, DECISION), and participated in Incure and Casym.
Since DGC started in Life Sciences he has been working in two interlinked fields: Translational Bioinformatics and Systems Biology. A recent significant achievement was developing multi-omic integration tools implemented in the Bioconductor STATegRa package. DGC's main aims now are (i) developing single-cell and spatial multi-omic analysis tools and (ii) the mechanistic understanding of the Bone Marrow Nice and Liver in health and disease.