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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jinhong Shi Bolin Chen Fang-Xiang Wu |
| Copyright Year | 2011 |
| Abstract | A new method is presented to estimate the accuracy of peptide identification with logistic regression (LR)based on Sequest scores. Each peptide is characterized with the regularized Sequest scores ?Cn* and Xcorr*. The score regularization is formulated as an optimization problem by applying two assumptions: the smoothing consistency between sibling peptides and the fitting consistency between original scores and new scores. An adjacency matrix is built to describe the affinity between peptides, and is used in the score regularization to compute new scores. Then, the new scores are input to the LR model, which is solved with the penalized Newton-Raph son method. By applying the method on two datasets with known validity, the results have shown that the proposed method can robustly assign accurate probabilities to peptides and have a very high discrimination power, higher than that of Peptide Prophet, to distinguish correct and incorrect peptides. and have a very high discrimination power, higher than that of PeptideProphet, to distinguish correct and incorrect peptides. |
| Starting Page | 191 |
| Ending Page | 196 |
| File Size | 267792 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781457717994 |
| DOI | 10.1109/BIBM.2011.19 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-11-12 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Proteins Smoothing methods Accuracy score regularization Peptides mass spectrometry peptide identification Vectors Helium logistic regression consistency Logistics |
| Content Type | Text |
| Resource Type | Article |
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