Biomarker discovery and redundancy reduction towards classification using a multi-factorial MALDI-TOF MS T2DM mouse model dataset
Data(s) |
09/05/2011
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Resumo |
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. |
Formato |
text |
Identificador |
http://centaur.reading.ac.uk/22025/1/1471-2105-12-140.pdf Bauer, C., Kleinjung, F., Smith, C. J. <http://centaur.reading.ac.uk/view/creators/90003936.html>, Towers, M. W., Tiss, A. <http://centaur.reading.ac.uk/view/creators/90002793.html>, Chadt, A., Dreja, T., Beule, D., Al-Hasani, H., Reinert, K., Schuchhardt, J. and Cramer, R. <http://centaur.reading.ac.uk/view/creators/90000907.html> (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 <http://dx.doi.org/10.1186/1471-2105-12-140> |
Idioma(s) |
en |
Publicador |
BioMed Central |
Relação |
http://centaur.reading.ac.uk/22025/ creatorInternal Smith, Celia J. creatorInternal Tiss, Ali creatorInternal Cramer, Rainer http://dx.doi.org/10.1186/1471-2105-12-140 10.1186/1471-2105-12-140 |
Tipo |
Article PeerReviewed |