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Enter the Matrix: Factorization Uncovers Knowledge from Omics
Authors:Genevieve L. Stein-O’Brien  Raman Arora  Aedin C. Culhane  Alexander V. Favorov  Lana X. Garmire  Casey S. Greene  Loyal A. Goff  Yifeng Li  Aloune Ngom  Michael F. Ochs  Yanxun Xu  Elana J. Fertig
Affiliation:1. Department of Oncology, Division of Biostatistics and Bioinformatics, Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD, USA;2. Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA;3. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD, USA;4. Department of Computer Science, Institute for Data Intensive Engineering and Science, Johns Hopkins University, Baltimore, MD, USA;5. Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA;6. Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, USA;7. Vavilov Institute of General Genetics, Moscow, Russia;8. University of Hawaii Cancer Center, Honolulu, HI, USA;9. Department of Systems Pharmacology and Translational Therapeutics, Perelman School of Medicine, University of Pennsylvania, PA, USA;10. Childhood Cancer Data Lab, Alex’s Lemonade Stand Foundation, PA, USA;11. Digital Technologies Research Centre, National Research Council of Canada, Ottawa, ON, Canada;12. School of Computer Science, University of Windsor, Windsor, ON, Canada;13. Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA;14. Department of Applied Mathematics and Statistics, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, USA
Abstract:
Keywords:genomics  unsupervised learning  matrix factorization  dimension reduction  single cell  deconvolution
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