Comparison of unsupervised and supervised gene selection methods.
Modern machine learning methods based on matrix decomposition techniques like Independent Component Analysis (ICA) provide new and efficient analysis tools which are currently explored to analyze gene expression profiles. These exploratory feature extraction techniques yield informative expression modes (ICA) which are considered indicative of underlying regulatory processes. Their most strongly expressed genes represent marker genes for classification of the tissue samples under investigation. Comparison with supervised gene selection methods based on statistical scores or support vector machines corroborate these findings. The method is applied to macrophages loaded/de-loaded with chemically modified low density lipids.
Herold
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D
Institute for Biophysics, CIML Group, University of Regensburg, D-93040, Germany.
Lutter
D
D
Schachtner
R
R
Tome
A M
AM
Schmitz
G
G
Lang
E W
EW
D000465:Algorithms; D001185:Artificial Intelligence; D050197:Atherosclerosis / Q000097:blood* / Q000175:diagnosis; D015415:Biomarkers / Q000097:blood; D001798:Blood Proteins / Q000032:analysis*; D002478:Cells, Cultured; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009000:Monocytes / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
D015415:Biomarkers; D001798:Blood Proteins