Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset
Bauer, C., Kleinjung, F., Smith, C. J., Towers, M. W., Tiss, A., Chadt, A., Dreja, T., Beule, D., Al-Hasani, H., Reinert, K., Schuchhardt, J. and Cramer, R.
It is advisable to refer to the publisher's version if you intend to cite from this work. See Guidance on citing. To link to this item DOI: 10.1186/1471-2105-12-140 Abstract/SummaryDiabetes 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.
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