Saturday, 22 July 2017

Sunday, 23 July 2017

Lodewyk Wessels, PhD

Professor in Molecular Carcinogenesis Division, Netherlands Cancer Institute
Presentation Title: Molecular networks as determinants of response and outcome

Abstract: Precision medicine requires the mapping of clinical and molecular variables to response to (combination) treatment. The exact mechanisms involved in tumor development and therapy response, however, are still largely unclear. Here we report on two computational approaches we developed to systematically unravel these mechanisms based on high throughput datasets, and show these can be employed to predict response to anti-cancer agents. Our results obtained on the TCGA pan-cancer data and the GDSC1000 cell line drug screen bring precision medicine a step closer.

Short Bio: Prof. Lodewyk Wessels is the head of the Computational Cancer Biology group at the Netherlands Cancer Institute in Amsterdam, The Netherlands. His group focuses on developing novel computational approaches to exploit a wide variety of data sources to map out the cancer landscape, unravel its regulation and employ this knowledge the develop strategies for personalized treatment. Dr Wessels received his M.Sc. and Ph.D. both from the Department of Electronic and Computer Engineering, University of Pretoria, South Africa. From 1993 to 1997 he was a member of the Center for Spoken Language Understanding at the Oregon Graduate School of Science and Technology. In 1997 he joined the Faculty of Electrical Engineering, Mathematics and Computer Science at the Delft University of Technology and was appointed assistant professor in 2002. In 2006 he became a faculty member at the Netherlands Cancer Institute in Amsterdam, The Netherlands. He was appointed chair of Computational Cancer Biology at the Delft University of Technoilogy in 2012 and in 2016 as Deputy Director Research of the Netherlands Cancer Institute

Cesare Furlanello, PhD

Head of Predictive Models for Biomedicine & Environment, Fondazione Bruno Kessler, Italy
Presentation Title: Towards a scientific blockchain framework for reproducible data analysis

Short Bio: Cesare Furlanello is senior researcher at the Kessler Foundation (Trento, Italy), head of Complex Data Analytics and of the Predictive Models for Biomedicine and Environment research unit. He is a data scientist, active in the field of bioinformatics, combining machine learning methods with new data analytics infrastructures. He is a PI collaborator of international consortia such as the SEQC/MAQC FDA initiative and the RIKEN FANTOM5 project and President-Elect of the newly founded Massive Data Analysis and Quality Control (MAQC) society. His most recent research is directed to deep learning applied on integrative omics, imaging and phenotypic data, with a focus on scientific reproducibility.