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 apply the developed methods in different domains including metabolomics and social network analysis.


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



  • Rasmus Bro, Faculty of Science, University of Copenhagen, Denmark
  • Mathias Nilsson, School of Chemistry, The University of Manchester, UK
  • Lars Ove Dragsted, Faculty of Science, University of Copenhagen, Denmark
  • Gozde Gurdeniz, 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
  • Beyza Ermis, Bogazici University, Istanbul, Turkey
  • Bulent Yener, Rensselaer Polytechnic Institute, Troy, NY
  • Vagelis Papalexakis, Carnegie Mellon University, Pittsburgh, PA
  • Age K. Smilde, University of Amsterdam, Netherlands




















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