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BMCBI
2011

Biomarker Discovery and Redundancy Reduction towards Classification using a Multi-factorial MALDI-TOF MS T2DM Mouse Model Datase

10 years 3 months ago
Biomarker Discovery and Redundancy Reduction towards Classification using a Multi-factorial MALDI-TOF MS T2DM Mouse Model Datase
Background: Diabetes like many diseases and biological processes is not mono-causal. On the one hand multifactorial studies with complex experimental design are required for its comprehensive analysis. On the other hand, the data from these studies often include a substantial amount of redundancy such as proteins that are typically represented by a multitude of peptides. Coping simultaneously with both complexities (experimental and technological) makes data analysis a challenge for Bioinformatics. Results: We present a comprehensive work-flow tailored for analyzing complex data including data from multifactorial studies. The developed approach aims at revealing effects caused by a distinct combination of experimental factors, in our case genotype and diet. Applying the developed work-flow to the analysis of an established polygenic mouse model for diet-induced type 2 diabetes, we found peptides with significant fold changes exclusively for the combination of a particular strain and d...
Chris Bauer, Frank Kleinjung, Celia J. Smith, Mark
Added 24 Aug 2011
Updated 24 Aug 2011
Type Journal
Year 2011
Where BMCBI
Authors Chris Bauer, Frank Kleinjung, Celia J. Smith, Mark W. Towers, Ali Tiss, Alexandra Chadt, Tanja Dreja, Dieter Beule, Hadi Al-Hasani, Knut Reinert, Johannes Schuchhardt, Rainer Cramer
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