Research article
Statistical learning of peptide retention behavior in chromatographic separations: a new kernel-based approach for computational proteomics
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* Corresponding author: Nico Pfeifer npfeifer@informatik.uni-tuebingen.de
1 Division for Simulation of Biological Systems, Center for Bioinformatics, Eberhard-Karls University, 72076 Tübingen, Germany
2 Department of Chemistry, Instrumental Analysis and Bioanalysis, Saarland University, 66123 Saarbrücken, Germany
BMC Bioinformatics 2007, 8:468 doi:10.1186/1471-2105-8-468
Published: 30 November 2007Additional files
Additional file 1:
Verified data set one (vds1). vds1.csv lists the identified peptides of vds1 with normalized retention time, observed retention time, precursor mass, charge, score and significance threshold score (at significance level p = 0.05).
Format: CSV Size: 6KB Download file
Additional file 2:
Verified data set two (vds2). vds2.csv lists the identified peptides of vds2 with normalized retention time, observed retention time, precursor mass, charge, score and significance threshold score (at significance level p = 0.05).
Format: CSV Size: 6KB Download file
Additional file 3:
Verified data set three (vds3). vds3.csv lists the identified peptides of vds3 with normalized retention time, observed retention time, precursor mass, charge, score and significance threshold score (at significance level p = 0.05).
Format: CSV Size: 5KB Download file