Plate IV.666 · Publications
How to extract marker genes from microarray data sets
Conference paper in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, August 2007.
Abstract
In this study we focus on classification tasks and apply matrix factorization techniques like principal component analysis (PCA), independent component analysis (ICA) and non-negative matrix factorization (NMF) to a microarray data set. The latter monitors the gene expression levels (GEL) of mononcytes and macrophages during and after differentiation. We show that these tools are able to identify relevant signatures in the deduced matrices and extract marker genes from these gene expression profiles (GEPs) without the need for extensive data bank search for appropriate functional annotations. With these marker genes corresponding test data sets can then easily be classified into related diagnostic categories.
Cite
@inproceedings{schachtner2007how,
title = {{How to extract marker genes from microarray data sets}},
author = {Schachtner, R. and Lutter, D. and Theis, F. J. and Lang, E. W. and Schmitz, G. and Tome, A. M. and Vilda, P. Gomez},
booktitle = {2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society},
publisher = {IEEE},
year = {2007},
month = aug,
doi = {10.1109/iembs.2007.4353266},
url = {https://doi.org/10.1109/iembs.2007.4353266},
pmid = {18002932}
}