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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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Abstract

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
Date:2008
Institutions:Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang
Identification Number:
ValueType
10.1093/bioinformatics/btn245DOI
Subjects:500 Science > 570 Life sciences
Status:Published
Refereed:Unknown
Created at the University of Regensburg:Unknown
Owner: Gertraud Kellers
Deposited On:05 Oct 2010 06:34
Last Modified:05 Oct 2010 06:34
Item ID:16918
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