Lutter, D. and Langmann, Th. and Ugocsai, P. and Moehle, C. and Seibold, E. and Splettstoesser, W. D. and Gruber, P. and Lang, E. W. and Schmitz, G. (2009) Analyzing time-dependent microarray data using independent component analysis derived expression modes from human macrophages infected with F. tularensis holartica. JOURNAL OF BIOMEDICAL INFORMATICS, 42 (4). pp. 605-611. ISSN 1532-0464,
Full text not available from this repository. (Request a copy)Abstract
The analysis of large-scale gene expression profiles is still a demanding and extensive task. Modern machine learning and data mining techniques developed in linear algebra, like Independent Component Analysis (ICA), become increasingly popular as appropriate tools for analyzing microarray data. We applied ICA to analyze kinetic gene expression profiles of human monocyte derived macrophages (MDM) from three different donors infected with Francisella tularensis holartica and compared them to more classical methods like hierarchical clustering. Results were compared using a pathway analysis too[, based on the Gene Ontology and the MeSH database. We could show that both methods lead to time-dependent gene regulatory patterns which fit well to known TNF alpha induced immune responses. In comparison, the nonexclusive attribute of ICA results in a more detailed view and a higher resolution in time dependent behavior of the immune response genes. Additionally, we identified NF kappa B as one of the main regulatory genes during response to F. tularensis infection. (C) 2009 Elsevier Inc. All rights reserved
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | GENE-EXPRESSION; LVS; ACTIVATION; Independent component analysis; Microarray; Gene chip; Infection; Time course; Clustering; Francisella tularensis |
| Subjects: | 500 Science > 570 Life sciences 600 Technology > 610 Medical sciences Medicine |
| Divisions: | Medicine > Lehrstuhl für Klinische Chemie und Laboratoriumsmedizin Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 10 Sep 2020 13:04 |
| Last Modified: | 10 Sep 2020 13:04 |
| URI: | https://pred.uni-regensburg.de/id/eprint/28643 |
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