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Autor(en):
Schachtner, R.; Lutter, D.; Knollmüller, P.; Tomé, A. M.; Theis, F. J.; Schmitz, G.; Stetter, M.; Vilda, P. G.; Lang, E. W. 
Titel:
Knowledge-based gene expression classification via matrix factorization 
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...    »
 
Zeitschriftentitel:
Bioinformatics 
Jahr:
2008 
Band / Volume:
24 
Heft / Issue:
15 
Seitenangaben Beitrag:
1688-1697