Knowledge-based matrix factorization temporally resolves the cellular responses to IL-6 stimulation
External stimulations of cells by hormones, cytokines or growth factors activate signal transduction pathways that subsequently induce a re-arrangement of cellular gene expression. The analysis of such changes is complicated, as they consist of multi-layered temporal responses. While classical analy...
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| Main Authors: | , , , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
30 November 2010
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| In: |
BMC bioinformatics
Year: 2010, Volume: 11, Pages: 1-14 |
| ISSN: | 1471-2105 |
| DOI: | 10.1186/1471-2105-11-585 |
| Online Access: | Resolving-System, kostenfrei, Volltext: https://doi.org/10.1186/1471-2105-11-585 Verlag, kostenfrei, Volltext: https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-11-585 
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| Author Notes: | Andreas Kowarsch, Florian Blöchl, Sebastian Bohl, Maria Saile, Norbert Gretz, Ursula Klingmüller, Fabian J. Theis |
| Summary: | External stimulations of cells by hormones, cytokines or growth factors activate signal transduction pathways that subsequently induce a re-arrangement of cellular gene expression. The analysis of such changes is complicated, as they consist of multi-layered temporal responses. While classical analyses based on clustering or gene set enrichment only partly reveal this information, matrix factorization techniques are well suited for a detailed temporal analysis. In signal processing, factorization techniques incorporating data properties like spatial and temporal correlation structure have shown to be robust and computationally efficient. However, such correlation-based methods have so far not be applied in bioinformatics, because large scale biological data rarely imply a natural order that allows the definition of a delayed correlation function. |
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| Item Description: | Gesehen am 22.03.2023 |
| Physical Description: | Online Resource |
| ISSN: | 1471-2105 |
| DOI: | 10.1186/1471-2105-11-585 |