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This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement 764281. Copyright ©2018 by AiPBAND

Jeremy Georges-Filteau

ESR-12

Topic: 

Cloud-based brain cancer diagnostic platform and infrastructure

Supervisor: 

Dr Marinel Cavelaars, The HYVE, Netherlands

Jeremy obtained a bachelor’s of bioinformatics from Université Laval during which he did an exchange at Université de Strasbourg in the Master’s of Integrative structural biology and bioinformatics and completed an internship at The Hyve developing a user interface prototype for TranSMART. Following this, he completed a Master’s in computer science at McGill University by developing a machine learning algorithm based on phylogenetic networks that has significant advantages over commonly used genetic assignment methods. Jeremy joined The Hyve in 2018 to pursue a PhD within the context of the AiPBAND project and, more generally, his passion for multidisciplinary science.

The objective of this ESR project is to develop a big-data powered diagnostic infrastructure for prediction of brain cancer which will be fully deployed and run on cloud services (e.g. Amazon EC2). The platform, designed, built and deployed by the ESR, will include a data warehouse for the integration of highly structured clinical data with imaging data and biological data. Data generated by innovative biosensors, developed by WP2 will be stored in Arvados Keep, a core component from the Arvados platform (https://arvados.org/ ). The ESR also will design and implement highly efficient ETL (extract, transform, load) pipelines for automated data loading from multiple sources and investigate the best options to ensure high computational performance, security and scalability. The prediction algorithms, developed in close collaboration with ESR-11 at IC, will be executed using the Arvados Crunch framework, a workflow engine for complex analysis pipelines for very large data batches. By providing an API (application program interface) and an intuitive GUI (graphical user interface) for the diagnostic platform, data and prediction results can be immediately accessed by patients, clinicians and care providers via mobile devices, tablets or desktop computers and allow third party access for potential industrial exploitation.