Abstract
Background Understanding the proteome, the structure and function of each protein, and the interactions among proteins will give clues to search useful targets and biomarkers for pharmaceutical design. Peptide drift time prediction in IMMS will improve the confidence of peptide identification by limiting the peptide search space during MS/MS database searching and therefore reducing false discovery rate (FDR) of protein identification. A peptide drift time prediction method was proposed here using an artificial neural networks (ANN) regression model. We test our proposed model on three peptide datasets with different charge state assignment (see Table 1). The results can be found in Figure 1, where a higher prediction performance was achieved, over 0.9 for CI and C2, as well as 0.75 for C3.
Highlights
Understanding the proteome, the structure and function of each protein, and the interactions among proteins will give clues to search useful targets and biomarkers for pharmaceutical design
UT-ORNL-KBRIN Bioinformatics Summit 2009 Eric C Rouchka and Julia Krushkal Meeting abstracts – A single PDF containing all abstracts in this Supplement is available here. http://www.biomedcentral.com/content/pdf/1471-2105-10-S7-info.pdf
Peptide drift time prediction in IMMS will improve the confidence of peptide identification by limiting the peptide search space during MS/MS database searching and reducing false discovery rate (FDR) of protein identification
Summary
Address: 1Department of Chemistry, University of Louisville, Louisville, KY 40292, USA, 2Predictive Physiology and Medicine Inc. UT-ORNL-KBRIN Bioinformatics Summit 2009 Eric C Rouchka and Julia Krushkal Meeting abstracts – A single PDF containing all abstracts in this Supplement is available here. http://www.biomedcentral.com/content/pdf/1471-2105-10-S7-info.pdf
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have