Abstract
Exploratory matrix factorization methods like PCA, ICA and sparseNMF are applied to identify marker genes and classify gene expression data sets into different categories for diagnostic purposes or group genes into functional categories for further investigation of related regulatory pathways. Gene expression levels of either human breast cancer (HBC) cell lines [6] or the famous leucemia data set [10] are considered. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Schachtner, R., Lutter, D., Theis, F. J., Lang, E. W., Tomé, A. M., Gorriz Saez, J. M., & Puntonet, C. G. (2007). Blind matrix decomposition techniques to identify marker genes from microarrays. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4666 LNCS, pp. 649–656). Springer Verlag. https://doi.org/10.1007/978-3-540-74494-8_81
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