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Comparison of unsupervised and supervised gene selection methods

Herold, Daniela, Lutter, D., Schachtner, R., Tome, A. M., Schmitz, G. and Lang, E. W. (2008) Comparison of unsupervised and supervised gene selection methods. Conf Proc IEEE Eng Med Biol Soc 2008, pp. 5212-5215.

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Abstract

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 ...

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Item type:Article
Date:2008
Institutions:Medicine > Institut für Funktionelle Genomik > Lehrstuhl für Funktionelle Genomik (Prof. Oefner)
Identification Number:
ValueType
10.1109/IEMBS.2008.4650389DOI
19163892PubMed ID
Dewey Decimal Classification:600 Technology > 610 Medical sciences Medicine
Status:Published
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:Partially
Item ID:34368
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