{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T10:13:16Z","timestamp":1767262396414},"reference-count":31,"publisher":"IGI Global","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2012,7,1]]},"abstract":"<p>Metabolomics focuses on the detection of chemical substances in biological fluids such as urine and blood using a number of analytical techniques including Nuclear Magnetic Resonance (NMR) spectroscopy and Liquid Chromatography-Mass Spectrometry (LC-MS). Among the major challenges in analysis of metabolomics data are (i) joint analysis of data from multiple platforms, and (ii) capturing easily interpretable underlying patterns, which could be further utilized for biomarker discovery. In order to address these challenges, the authors formulate joint analysis of data from multiple platforms as a coupled matrix factorization problem with sparsity penalties on the factor matrices. They developed an all-at-once optimization algorithm, called CMF-SPOPT (Coupled Matrix Factorization with SParse OPTimization), which is a gradient-based optimization approach solving for all factor matrices simultaneously. Using numerical experiments on simulated data, the authors demonstrate that CMF-SPOPT can capture the underlying sparse patterns in data. Furthermore, on a real data set of blood samples collected from a group of rats, the authors use the proposed approach to jointly analyze metabolomics data sets and identify potential biomarkers for apple intake. Advantages and limitations of the proposed approach are also discussed using illustrative examples on metabolomics data sets.<\/p>","DOI":"10.4018\/jkdb.2012070102","type":"journal-article","created":{"date-parts":[[2013,6,20]],"date-time":"2013-06-20T16:03:53Z","timestamp":1371744233000},"page":"22-43","source":"Crossref","is-referenced-by-count":11,"title":["Coupled Matrix Factorization with Sparse Factors to Identify Potential Biomarkers in Metabolomics"],"prefix":"10.4018","volume":"3","author":[{"given":"Evrim","family":"Acar","sequence":"first","affiliation":[{"name":"Department of Food Science, Faculty of Science, University of Copenhagen, Copenhagen, Denmark"}]},{"given":"Gozde","family":"Gurdeniz","sequence":"additional","affiliation":[{"name":"Department of Human Nutrition, Faculty of Science, University of Copenhagen, Copenhagen, Denmark"}]},{"given":"Morten A.","family":"Rasmussen","sequence":"additional","affiliation":[{"name":"Department of Food Science, Faculty of Science, University of Copenhagen, Copenhagen, Denmark"}]},{"given":"Daniela","family":"Rago","sequence":"additional","affiliation":[{"name":"Department of Human Nutrition, Faculty of Science, University of Copenhagen, Copenhagen, Denmark"}]},{"given":"Lars O.","family":"Dragsted","sequence":"additional","affiliation":[{"name":"Department of Human Nutrition, Faculty of Science, University of Copenhagen, Copenhagen, Denmark"}]},{"given":"Rasmus","family":"Bro","sequence":"additional","affiliation":[{"name":"Department of Food Science, Faculty of Science, University of Copenhagen, Copenhagen, Denmark"}]}],"member":"2432","reference":[{"key":"jkdb.2012070102-0","doi-asserted-by":"publisher","DOI":"10.1002\/cem.1335"},{"key":"jkdb.2012070102-1","doi-asserted-by":"crossref","unstructured":"Acar, E., Gurdeniz, G., Rasmussen, M. A., Rago, D., Dragsted, L. O., & Bro, R. (2012). Coupled matrix factorization with sparse factors to identify potential biomarkers in metabolomics. In Proceedings of the ICDM Workshop on Biological Data Mining and its Applications in Healthcare. (pp. 108)","DOI":"10.1109\/ICDMW.2012.17"},{"key":"jkdb.2012070102-2","unstructured":"Acar, E., Kolda, T. G., & Dunlavy, D. M. (2011b). All-at-once optimization for coupled matrix and tensor factorizations. KDD Workshop on Mining and Learning with Graphs."},{"key":"jkdb.2012070102-3","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2008.112"},{"key":"jkdb.2012070102-4","unstructured":"Badea, L. (2008). Extracting gene expression profiles common to colon and pancreatic adenocarcinoma using simultaneous nonnegative matrix factorization. Pacific Symposium on Biocomputing, 13, 279\u2013290."},{"key":"jkdb.2012070102-5","doi-asserted-by":"crossref","unstructured":"Banerjee, A., Basu, S., & Merugu, S. (2007). Multi-way clustering on relation graphs. SIAM. In Proceedings of the International Conference on Data Mining (SDM) (pp.145-156).","DOI":"10.1137\/1.9781611972771.14"},{"key":"jkdb.2012070102-6","doi-asserted-by":"crossref","unstructured":"Buchanan, A. M., & Fitzgibbon, A. W. (2005). Damped newton algorithms for matrix factorization with missing data. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 316\u2013322).","DOI":"10.1109\/CVPR.2005.118"},{"key":"jkdb.2012070102-7","doi-asserted-by":"publisher","DOI":"10.1207\/S15327914NC431_1"},{"key":"jkdb.2012070102-8","doi-asserted-by":"publisher","DOI":"10.1016\/j.aca.2011.03.025"},{"key":"jkdb.2012070102-9","doi-asserted-by":"crossref","unstructured":"Dunlavy, D. M., Kolda, T. G., & Acar, E. (2010). Poblano v1.0: A Matlab toolbox for gradient-based optimization (Tech. Rep. No. SAND2010-1422). Sandia National Laboratories.","DOI":"10.2172\/989350"},{"key":"jkdb.2012070102-10","author":"G. H.Golub","year":"1996","journal-title":"Matrix computations"},{"key":"jkdb.2012070102-11","doi-asserted-by":"publisher","DOI":"10.3390\/metabo2010077"},{"key":"jkdb.2012070102-12","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/28.3-4.321"},{"key":"jkdb.2012070102-13","doi-asserted-by":"publisher","DOI":"10.1007\/s11306-009-0181-3"},{"key":"jkdb.2012070102-14","first-page":"556","article-title":"Algorithms for non-negative matrix factorization.","volume":"13","author":"D. D.Lee","year":"2001","journal-title":"Advances in Neural Information Processing Systems"},{"key":"jkdb.2012070102-15","unstructured":"Lee, S., Lee, H., Abbeel, P., & Ng, A. Y. (2006). Efficient l1 regularized logistic regression. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 401\u2013408."},{"key":"jkdb.2012070102-16","doi-asserted-by":"publisher","DOI":"10.1007\/BF02289472"},{"key":"jkdb.2012070102-17","doi-asserted-by":"crossref","unstructured":"Lin, Y. R., Sun, J., Castro, P., Konuru, R., Sundaram, H., & Kelliher, A. (2009). Metafac: Community discovery via relational hypergraph factorization. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (pp. 527-536).","DOI":"10.1145\/1557019.1557080"},{"key":"jkdb.2012070102-18","doi-asserted-by":"crossref","unstructured":"Long, B., Zhang, Z., Wu, X., & Yu, P. S. (2006). Spectral clustering for multi-type relational data. International Conference on Machine Learning (ICML) (pp. 585\u2013592).","DOI":"10.1145\/1143844.1143918"},{"key":"jkdb.2012070102-19","doi-asserted-by":"crossref","unstructured":"Ma, H., Yang, H., Lyu, M. R., & King, I. (2008). Sorec: Social recommendation using probabilistic matrix factorization. In Proceedings of the ACM International Conference on Information and Knowledge Management (CIKM) (pp. 931-940).","DOI":"10.1145\/1458082.1458205"},{"key":"jkdb.2012070102-20","author":"J.Nocedal","year":"2006","journal-title":"Numerical optimization"},{"key":"jkdb.2012070102-21","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0028072"},{"key":"jkdb.2012070102-22","doi-asserted-by":"publisher","DOI":"10.1080\/01635581.2011.535961"},{"key":"jkdb.2012070102-23","doi-asserted-by":"publisher","DOI":"10.1016\/j.chemolab.2010.07.006"},{"key":"jkdb.2012070102-24","doi-asserted-by":"crossref","unstructured":"Singh, A. P., & Gordon, G. J. (2008). Relational learning via collective matrix factorization. In Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (pp. 650\u2013658).","DOI":"10.1145\/1401890.1401969"},{"key":"jkdb.2012070102-25","doi-asserted-by":"publisher","DOI":"10.1002\/cem.811"},{"issue":"1","key":"jkdb.2012070102-26","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the lasso.","volume":"58","author":"R.Tibshirani","year":"1996","journal-title":"Journal of the Royal Statistical Society. Series B. Methodological"},{"key":"jkdb.2012070102-27","doi-asserted-by":"publisher","DOI":"10.1016\/j.aca.2009.08.029"},{"key":"jkdb.2012070102-28","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-10-246"},{"key":"jkdb.2012070102-29","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2105-12-448"},{"key":"jkdb.2012070102-30","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2002.1011195"}],"container-title":["International Journal of Knowledge Discovery in Bioinformatics"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=77809","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,12]],"date-time":"2024-05-12T17:57:41Z","timestamp":1715536661000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/jkdb.2012070102"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2012,7,1]]},"references-count":31,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2012,7]]}},"URL":"https:\/\/doi.org\/10.4018\/jkdb.2012070102","relation":{},"ISSN":["1947-9115","1947-9123"],"issn-type":[{"value":"1947-9115","type":"print"},{"value":"1947-9123","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,7,1]]}}}