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Work Package 5: Data Analytics for Evaluation of Therapies


WP5 will work on providing the means for analyzing complex data sets comprised of different layers of information. This will enable molecular and clinical data to be linked to predicting outcomes. Its activity will be centered on the description, analysis, and modeling of the data collected, which will then be generated into a personalized medicine framework. Tools and models will be implemented to answer the most important disease-specific questions and will be applicable to cross-disease analyses. 


WP5 Partners

AEMPS, Amgen, AP-HP, Barts Health, Bayer, BFArM, EBMT, ELN, EORTC, Erasmus MC, ERIC, FISM Onlus, GFM, GMV, Goethe University, GPOH, GRAALL, GRL-SANGER, HHU, HULAFE, IECSCYL-IBSAL, IJC, Janssen, LeukaNET, LMU, LYSA, MediSapiens, MenarinI, MLL, MU, Newcastle University, NICE, Novartis, OPBG, Takeda, Ulm University, UNIBO, UNITO, University of Cambridge, University of Helsinki, University of Navarra, University of Rome Tor Vergata, University of York, VHIO, VIB, Vumc.

Achievements WP5

Considerable progress has been made in terms of establishing the Big Data platform and the methodology for analyzing data:

Achievements 2018:

Achievements 2017:

  • HARMONY's Big Data platform has been established;
  • A common data model that adheres to the FAIR (findable, accessible, interoperable, and reusable) data-sharing principles has been created;
  • HARMONY has begun developing and testing models based on available data on AML (TCGA public data, UNIBO internal data and Sanger Institute data);
  • HARMONY has started analyzing the description of datasets;
  • Methods working on methods for gene network analyses are beginning to be developed.

Outlook 2019

WP5 will focus on demonstrating the value of harmonizing heterogeneous HM patient data sources: developing novel analytical algorithms to identify molecular predictors of clinical outcomes and efficacious therapies, trends in economic and social impacts and improve patient quality of life.

  • Take steps to create small subgroups for each research project.
  • Review and formulation of research questions.
  • Given that real data is being utilized by outside users, further requirements/tools need to be created.