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. ORCID: https://orcid.org/0000-0002-8037-2511 (2011) Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset. BMC Bioinformatics, 12. 140. ISSN 1471-2105 doi: 10.1186/1471-2105-12-140

Abstract/Summary

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.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/22025
Identification Number/DOI	10.1186/1471-2105-12-140
Refereed	Yes
Divisions	Life Sciences Life Sciences > School of Chemistry, Food and Pharmacy > Department of Chemistry Interdisciplinary centres and themes > Chemical Analysis Facility (CAF)
Publisher	BioMed Central
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