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Title:

Knowledge-based gene expression classification via matrix factorization

Author(s):
Schachtner, R.; Lutter, D.; Knollmüller, P.; Tomé, A. M.; Theis, F. J.; Schmitz, G.; Stetter, M.; Vilda, P. G.; Lang, E. W.
Abstract:
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 features are considered indicative of underlying regulatory processes. They can as well be applied to the classification...     »
Journal title:
Bioinformatics
Year:
2008
Journal volume:
24
Journal issue:
15
Pages contribution:
1688-1697
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