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Knowledge-based gene expression classification via matrix factorization

Schachtner, R. and Lutter, D. and Knollmüller, P. and Tomé, A. M. and Theis, Fabian J. and Schmitz, G. and Stetter, M. and Gómez Vilda, P. and Lang, Elmar (2008) Knowledge-based gene expression classification via matrix factorization. Bioinformatics 24 (15), pp. 1688-1697.

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Motivation: Modern machine learning methods based on matrix decomposition techniques, like independent component analysis (ICA) or non-negative matrix factorization (NMF), provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield expression modes (ICA) or metagenes (NMF). These extracted ...


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Item Type:Article
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
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Dewey Decimal Classification:500 Science > 570 Life sciences
Created at the University of Regensburg:Unknown
Deposited On:05 Oct 2010 06:34
Last Modified:05 Oct 2010 06:34
Item ID:16918
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