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Scavager: A Versatile Postsearch Validation Algorithm for Shotgun Proteomics Based on Gradient Boosting
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DOI：10.1002/pmic.201800280.png)
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Abstract
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Shotgun proteomics workflows for database protein identification typically include a combination of search engines and postsearch validation software based mostly on machine learning algorithms. Here, a new postsearch validation tool called Scavager employing CatBoost, an open-source gradient boosting library, which shows improved efficiency compared with the other popular algorithms, such as Percolator, PeptideProphet, and Q-ranker, is presented. The comparison is done using multiple data sets and search engines, including MSGF+, MSFragger, X!Tandem, Comet, and recently introduced IdentiPy. Implemented in Python programming language, Scavager is open-source and freely available at https://bitbucket.org/markmipt/scavager.
Keywords:
machine learning
postsearch validation
proteomics
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