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TRANSYS PROJECT PHD STUDENT (R1)
BSC
Barcelona
About BSC
The Barcelona Supercomputing Center - Centro Nacional de Supercomputación (BSC-CNS) is the leading supercomputing center in Spain. It houses MareNostrum, one of the most powerful supercomputers in Europe, and is a hosting member of the PRACE European distributed supercomputing infrastructure. The mission of BSC is to research, develop and manage information technologies in order to facilitate scientific progress. BSC combines HPC service provision and R&D into both computer and computational science (life, earth and engineering sciences) under one roof, and currently has over 600 staff from 49 countries.

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Context And Mission
TranSYS, coordinated by K. Van Steen (KU Leuven), will recruit 15 ESRs (Early Stage Researchers) to highly skilled jobs in the new area of Systems Health developing tools and approaches to exploit large and complex datasets, to advance Precision (Personalised) Medicine in several disease areas. The training programme and experience of different international research environments cuts across traditional data and life sciences silos. The emphasis on translational research will support new collaborations between academics and the pharma and health analytics sectors. Our ESR projects will advance the state of the art on biomarker discovery, improve understanding of disease-specific molecular mechanism and target identification for optimal diagnostics, disease risk and treatment management, refine data generation and their management (including warehousing, disease specific and standardised approaches for data processing, visualisation and model development) leading to improved clinical study design, clinical sampling and more targeted therapeutics. This ETN (European Training Network) will internationalise participants, and leverage EC (European Commission) and industry sponsorship, to structure and expand the unique training programme and advance emerging research areas, combining wet-lab, clinical and Big Data resources with computational and modelling know-how.

PM proposes to individualize the practice of medicine based on patients’ genetic backgrounds, their biomarker characteristics and other omics datasets including exposure. ESR9 will build upon our previous work on network science, data integration and PM to propose a patient-centric data integration framework that enables all of the following: (1) improved patient stratification (allowing for predicting disease outcomes with more confidence), (2) uncovering molecular bases of diseases (molecular mechanisms, disease genes, biomarkers), and (3) personalized treatment predictions (drug repurposing). In practice, the mutational data will be mapped onto molecular networks and graphlet-based approaches will be utilized for mining for medically relevant signals. All data will be integrated using non-negative matrix factorization based approaches (Zitnik et al 2013; Gligorijevic et al. 2016) into a unified framework from which additional knowledge will be mined. Unlike existing approaches that only represent and integrate biological data as networks, we will thus also consider alternative data representation, such as hyper-networks and simplicial complexes that can capture the multi-scale organization of the data.
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