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...

• 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
• Keywords: Biometrics, BMCBI 2011, Distinct Combination, Maldi Tof Ms, Prevalence Of Diabetes