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Knowledge-based gene expression classification via matrix factorization

Schachtner, R., Lutter, D., Knollmüller, P., Tomé, A. M., Theis, Fabian J., Schmitz, G., Stetter, M., 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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Other URL: http://bioinformatics.oxfordjournals.org/content/24/15/1688.full.pdf+html


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
Item ID:16918
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