Exploiting blind matrix decomposition techniques to identify diagnostic marker genes

  • Authors:

  • Reinhard Schachtner;Dominik Lutter;Fabian J. Theis;Elmar W. Lang;Ana Maria Tomé;Gerd Schmitz

  • Affiliations:

  • Institute of Biophysics, University of Regensburg, Regensburg, Germany;Institute of Biophysics, University of Regensburg, Regensburg, Germany and Institute of Clinical Medicine, University Hospital Regensburg, Regensburg, Germany;Institute of Biophysics, University of Regensburg, Regensburg, Germany;Institute of Biophysics, University of Regensburg, Regensburg, Germany;DETI, IEETA Group, University of Aveiro, Aveiro, Portugal;Institute of Clinical Medicine, University Hospital Regensburg, Regensburg, Germany

  • Venue:
  • ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

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.