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The Good, the Bad and the Ugly: when statistics tells you to throw away peptide IDs

Reputable Mentor II
Reputable Mentor II
Siegfried Gessulat1, Tobias Schmidt2, Michael Graber1, Florian Seefried1, Dave Horn3, Christoph Henrich4, BernardDelanghe4, Daniel Zolg2, Mathias Wilhelm2, Bernhard Kuster2, Martin Frejno1
ASMS 2020
Purpose: Identify the optimal PSM validation method available. Methods: Comparison of different PSM validation methods and their performance in separating targets from decoys. Results: Semi-supervised machine learning using multiple scores can separate targets from decoys better than classical approaches based on a single score but there is room for improvement.

1msAId GmbH, Garching, Germany; 2Technical University of Munich, Freising, Germany; 3Thermo Fisher, San Jose, CA; 4Thermo Fisher Scientific (Bremen) GmbH, Bremen, Germany
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Last update:
‎10-15-2021 11:30 AM
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