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, University of California Riverside, CA
  • Age K. Smilde, University of Amsterdam, Netherlands
  • Tulay Adali, University of Maryland Baltimore County, MD









C8. Tensor-Based Fusion of EEG and FMRI to Understand Neurological Changes (E. Acar, Y. Levin-Schwartz, V. D. Calhoun, T. Adali), Submitted for Publication, 2016.

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.



T33. Steno Diabetes Center, Copenhagen, Denmark, October 5, 2016.

T32. 13th NuGOweek: Phenotypes and Prevention - The Interplay of Genes, Life-style factors and Gut Environment (Copenhagen, Denmark), September 5-8, 2016 (Invited Talk).

T31. 20th Conference of the International Linear Algebra Society (ILAS), Leuven, Belgium, July 11-15, 2016 (Invited Minisymposium Talk)

T30. SIAM Conf. Parallel Processing for Scientific Computing, Paris, France, April 12-15, 2016 (Invited Minisymposium Talk)

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

T28. Metabolomics Young Investigators Network Meeting, University of Copenhagen, March 30, 2016 (Invited Talk)

T27. The University of Edinburgh, Edinburgh, UK, March 17, 2016 (Invited Seminar)

T26. Workshop on Tensor Decompositions and Applications, Leuven, Belgium, January 21, 2016

T25. Ozyegin University, Istanbul, Turkey, December 29, 2015

T24. Nofima, Ås, Norway, November 5, 2015

T23. Yeditepe University, Istanbul, Turkey, September 8, 2015

T22. Bogazici University, Istanbul, Turkey, August 20, 2015

T21. JODA Workshop on data fusion methods and applications, Copenhagen, Denmark, August 12, 2015 (Organizer/Speaker)

T20. SCIX, Reno- Tahoe, Sept. 28- Oct.3, 2014 (Invited Minisymposium Talk)

T19. EUSIPCO, Lisbon, Portugal, September 2, 2014 (Refereed Paper)

T18. COMPSTAT, Geneva, Switzerland, August 19-22, 2014 (Invited Minisymposium Talk)

T17. KU Leuven, Belgium, June 2, 2014 (Invited Seminar)

T16. Arctic Analysis, Ilulissat, Greenland, March 10-14, 2014

T15. ERCIM 2013, University of London, UK, December 15, 2013 (Invited Minisymposium Talk)

T14. Gipsa-Lab, Grenoble, France, November 8, 2013 (Invited Seminar)

T13. Xerox Research Centre Europe, Grenoble, France, November 7, 2013 (Invited Seminar)

T12. Kadir Has University, Istanbul, Turkey, October 23, 2013

T11. Biomedical Engineering Society (BMES) Annual Meeting, Seattle, WA, September 28, 2013 (Invited Talk)

T10. IEEE EMBC, Osaka, Japan, July 7, 2013 (Refereed Paper)

T9. ICME Colloquium, Stanford University, CA, June 3, 2013

T8. SIAM Conference on Computational Science and Engineering (CSE), Boston, MA, Feb. 25-March 1, 2013 (Minisymposium Organizer/Speaker)

T7. SPIE Medical Imaging: Digital Pathology Conference, Orlando, FL, Feb. 10, 2013 (Refereed Paper)

T6. SISTA Workshop on Tensors and Data Analysis, KU Leuven, Belgium, December 13, 2012 (Invited Talk)

T5. ICDM Workshop on Biological Data Mining and its Applications in Healthcare, Brussels, Belgium, December 10, 2012 (Refereed Paper)

T4. SIAM Conference on Applied Linear Algebra, Valencia, Spain, June 18-22, 2012 (Invited Minisymposium Talk)

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

T2. Linear Algebra/Optimization Seminar , Stanford University, CA, May 10, 2012

T1. Sandia National Labs, Livermore, CA, May 7, 2012








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