Abstract
Exploratory matrix factorization methods like ICA and LNMF 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 [5] mediating bone metastasis or cell lines from Niemann Pick C patients monitoring monocyte - macrophage differentiation are considered.
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Schachtner, R., Lutter, D., Theis, F.J., Lang, E.W., Tomé, A.M., Schmitz, G. (2007). Exploiting Blind Matrix Decomposition Techniques to Identify Diagnostic Marker Genes. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_9
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DOI: https://doi.org/10.1007/978-3-540-74695-9_9
Publisher Name: Springer, Berlin, Heidelberg
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