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Abstracts of UT-ORNL-KBRIN (University of Tennessee-Oak Ridge National Laboratory-Kentucky Bioinformatics Network) Bioinformatics Summit 2009. Pikeville, Tennessee, USA. March 20-22, 2009.

- BMC Bioinformatics (2009)

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Fraction of peptides vs. prediction accuracy variation threshold. The diagram shows the number of peptides which can be predicted in different accuracy variation levels.
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Figure 1: Fraction of peptides vs. prediction accuracy variation threshold. The diagram shows the number of peptides which can be predicted in different accuracy variation levels.

Mentions: 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.


Abstracts of UT-ORNL-KBRIN (University of Tennessee-Oak Ridge National Laboratory-Kentucky Bioinformatics Network) Bioinformatics Summit 2009. Pikeville, Tennessee, USA. March 20-22, 2009.

- BMC Bioinformatics (2009)

Fraction of peptides vs. prediction accuracy variation threshold. The diagram shows the number of peptides which can be predicted in different accuracy variation levels.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3313255&req=5

Figure 1: Fraction of peptides vs. prediction accuracy variation threshold. The diagram shows the number of peptides which can be predicted in different accuracy variation levels.
Mentions: 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.

View Article: PubMed Central - HTML - PubMed

Show MeSH