By Topic

Statistical assessment for mass-spec protein identification using peptide fingerprinting approach

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

7 Author(s)
Ganapathy, A. ; Dept. of Comput. Sci., Missouri Univ., Columbia, MO, USA ; Wan, X.-F. ; Wan, J. ; Thelen, J.
more authors

We derive and validate a novel statistical model for confidence assessment of protein identification results using peptide mass fingerprint data. We simulate the digestion of the proteins and compare each peptide mass with the input mass. We compute scores from this matching of peptide and compute the distribution of scores for all the proteins in the database. Based on the distribution, we can provide the expectation value for a protein match in the database. We conclude that, given the complexity and noise of the data, the best method for effective confidence matching is using one scoring scheme for matching and another scoring scheme for confidence assessment.

Published in:

Engineering in Medicine and Biology Society, 2004. IEMBS '04. 26th Annual International Conference of the IEEE  (Volume:2 )

Date of Conference:

1-5 Sept. 2004