Joint Data Analysis for Enhanced Knowledge Discovery in Metabolomics

 

 

  

 

Recent technological advances enable us to collect huge amounts of data from multiple sources; however, extracting meaningful information remains to be the main challenge. In complex problems, the structure we are looking for is often buried in the data. In those cases, in particular, looking at the data from different aspects; in other words, jointly analyzing data from multiple sources, i.e., data fusion (also called multi-block, multi-view or multi-set data analysis), increases the chances of capturing the hidden dynamics. For instance, in metabolomics, biological fluids are measured using a variety of analytical techniques such as Liquid Chromatography-Mass Spectrometry (LC-MS), Gas Chromatography-Mass Spectrometry (GC-MS) and Nuclear Magnetic Resonance (NMR) Spectroscopy with an ultimate goal of identifying chemicals related to certain conditions such as diseases. Data measured using different analytical methods are often complementary and their fusion enhances knowledge discovery.

 

This project focuses on developing mathematical models/algorithms for data fusion. We plan to apply the developed methods in different domains including sensory data analysis, social network analysis and metabolomics.

 


 PI: Evrim Acar, Faculty of Science, University of Copenhagen, Denmark

 

 COLLABORATORS:

  • Rasmus Bro, Faculty of Science, University of Copenhagen, Denmark
  • Mathias Nilsson, Faculty of Science, University of Copenhagen, Denmark
  • Lars Ove Dragsted, Faculty of Science, University of Copenhagen, Denmark
  • Michael Saunders, Stanford University, Stanford, CA
  • Anne Tjønneland, Danish Cancer Society, Denmark
  • Tormod Næs, Nofima, Norway
  • Tamara G. Kolda, Sandia National Labs, Livermore, CA
  • A. Taylan Cemgil, Bogazici University, Istanbul, Turkey
  • Bulent Yener, Rensselaer Polytechnic Institute, Troy, NY

 


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This work is funded by the Danish Council for Independent Research | Technology and Production Sciences and Sapere Aude Program under the projects 11-116328 and 11-120947.

                   

                                                      


 Maintained by: Evrim Acar