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









C7. A Flexible Modeling Framework for Coupled Matrix and Tensor Factorizations (E. Acar, M. Nilsson, M. Saunders), EUSIPCO, 2014.

C6. Incorporating Higher Dimensionality in Joint Decomposition of EEG and fMRI (W. Swinnen, B. Hunyadi, E. Acar, S. Van Huffel, M. De Vos), EUSIPCO, 2014.

C5. Optimal Weight Learning for Coupled Tensor Factorization with Mixed Divergences (U. Simsekli, B. Ermis, A. T. Cemgil, E. Acar), EUSIPCO, 2013.

C4. Structure Revealing Data Fusion Model with Applications in Metabolomics (E. Acar, A. J. Lawaetz, M. A. Rasmussen, R. Bro), IEEE EMBC, pp. 6023-6026, 2013.

C3. Coupled Matrix Factorization with Sparse Factors to Identify Potential Biomarkers in Metabolomics (E. Acar, G. Gurdeniz, M. Rasmussen, D. Rago, L. Dragsted, R. Bro), ICDM Workshop on Biological Data Mining and its Applications in Healthcare, pp. 1-8, 2012.

C2. Link Prediction via Generalized Coupled Tensor Factorisation (B. Ermis, E. Acar, A. T. Cemgil), ECML/PKDD Workshop on Collective Learning and Inference on Structured Data, 2012.

C1. All-at-once Optimization for Coupled Matrix and Tensor Factorizations (E. Acar, T.G.Kolda and D.M. Dunlavy), KDD Workshop on Mining and Learning with Graphs, 2011.



J7. Common and Distinct Components in Data Fusion (A. K. Smilde, I. Mage, T. Naes, T. Hankemeier, M. A. Lips, H. A. L. Kiers, E. Acar and R. Bro), arXiv:1607.02328v1, Submitted for Publication, 2016.

J6. Data Fusion in Metabolomics using Coupled Matrix and Tensor Factorizations (E. Acar, R. Bro and A. K. Smilde), Proceedings of the IEEE, 103: 1602-1620, 2015.

J5. Link Prediction in Heterogeneous Data via Generalized Coupled Tensor Factorization (B. Ermis, E. Acar, A. T. Cemgil), Data Mining and Knowledge Discovery, 29(1): 203-236, 2015.

J4. Structure-Revealing Data Fusion (E. Acar, E. E. Papalexakis, G. Gurdeniz, M. A. Rasmussen, A. J. Lawaetz, M. Nilsson, R. Bro), BMC Bioinformatics, 15: 239, 2014.

J3. Understanding Data Fusion Within the Framework of Coupled Matrix and Tensor Factorizations (E. Acar, M. A. Rasmussen, F. Savorani, T. Naes, R. Bro), Chemometrics and Intelligent Laboratory Systems,  129: 53-63, 2013.

J2. Coupled Matrix Factorization with Sparse Factors to Identify Potential Biomarkers in Metabolomics (E. Acar, G. Gurdeniz, M. Rasmussen, D. Rago, L. Dragsted, R. Bro), International Journal of Knowledge Discovery in Bioinformatics, 3:22-43, 2012.

J1. Coupled Analysis of in Vitro and Histology Tissue Samples to Quantify Structure-Function Relationship (E. Acar, G. E. Plopper, B. Yener), PLoS One , 7(3): e32227, 2012.



T31. 20th Conference of the International Linear Algebra Society (ILAS), Leuven, Belgium, July 11-15, 2016 (Speaker: Evrim Acar)

T30. SIAM Conf. Parallel Processing for Scientific Computing, Paris, France, April 12-15, 2016 (Speaker: Evrim Acar)

T29. Dagstuhl Perspectives Workshop: Tensor Computing for Internet of Things, Schloss Dagstuhl, Germany, April 10-13, 2016 (Speaker: Evrim Acar)

T28. Metabolomics Young Investigators Network Meeting, University of Copenhagen, March 30, 2016 (Speaker: Evrim Acar)

T27. The University of Edinburgh, Edinburgh, UK, March 17, 2016 (Speaker: Evrim Acar)

T26. Workshop on Tensor Decompositions and Applications, Leuven, Belgium, January 21, 2016 (Speaker: Evrim Acar)

T25. Ozyegin University, Istanbul, Turkey, December 29, 2015 (Speaker: Evrim Acar)

T24. Nofima, Ås, Norway, November 5, 2015 (Speaker: Evrim Acar)

T23. Yeditepe University, Istanbul, Turkey, September 8, 2015 (Speaker: Evrim Acar)

T22. Bogazici University, Istanbul, Turkey, August 20, 2015 (Speaker: Evrim Acar)

T21. JODA Workshop on data fusion methods and applications, Copenhagen, Denmark, August 12, 2015 (Speaker: Evrim Acar)

T20. SCIX, Reno- Tahoe, Sept. 28- Oct.3, 2014 (Speaker: Vagelis Papalexakis)

T19. EUSIPCO, Lisbon, Portugal, September 2, 2014 (Speaker: Evrim Acar)

T18. COMPSTAT, Geneva, Switzerland, August 19-22, 2014 (Speaker: Evrim Acar)

T17. KU Leuven, Belgium, June 2, 2014 (Speaker: Evrim Acar)

T16. Arctic Analysis, Ilulissat, Greenland, March 10-14, 2014 (Speaker: Evrim Acar)

T15. ERCIM 2013, University of London, UK, December 15, 2013 (Speaker: Evrim Acar)

T14. Gipsa-Lab, Grenoble, France, November 8, 2013 (Speaker: Evrim Acar)

T13. Xerox Research Centre Europe, Grenoble, France, November 7, 2013 (Speaker: Evrim Acar)

T12. Kadir Has University, Istanbul, Turkey, October 23, 2013 (Speaker: Evrim Acar)

T11. Biomedical Engineering Society (BMES) Annual Meeting, Seattle, WA, September 28, 2013 (Speaker: Evrim Acar)

T10. IEEE EMBC, Osaka, Japan, July 7, 2013 (Speaker: Evrim Acar)

T9. ICME Colloquium, Stanford University, CA, June 3, 2013 (Speaker: Evrim Acar)

T8. SIAM Conference on Computational Science and Engineering (CSE), Boston, MA, Feb. 25-March 1, 2013 (Speaker: Evrim Acar)

T7. SPIE Medical Imaging: Digital Pathology Conference, Orlando, FL, Feb. 10, 2013 (Speaker: Evrim Acar)

T6. SISTA Workshop on Tensors and Data Analysis, KU Leuven, Belgium, December 13, 2012 (Speaker: Evrim Acar)

T5. ICDM Workshop on Biological Data Mining and its Applications in Healthcare, Brussels, Belgium, December 10, 2012 (Speaker: Evrim Acar)

T4. SIAM Conference on Applied Linear Algebra, Valencia, Spain, June 18-22, 2012 (Speaker: Evrim Acar)

T3. Three-way Methods in Chemistry and Psychology (TRICAP), Bruges, Belgium, June 2-7, 2012 (Speaker: Evrim Acar)

T2. Linear Algebra/Optimization Seminar , Stanford University, CA, May 10, 2012 (Speaker: Evrim Acar)

T1. Sandia National Labs, Livermore, CA, May 7, 2012 (Speaker: Evrim Acar)








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