on 06-20-202002:26 PM - edited on 10-15-202111:30 AM by Closed Account
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