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This article is part of the supplement: Statistical mass spectrometry-based proteomics

Open Access Research

A cross-validation scheme for machine learning algorithms in shotgun proteomics

Viktor Granholm1, William Stafford Noble23 and Lukas Käll4*

Author affiliations

1 Science for Life Laboratory, Department of Biochemistry and Biophysics, Stockholm University, Solna, Sweden

2 Department of Genome Sciences, University of Washington, Seattle, USA

3 Department of Computer Science and Engineering, University of Washington, Seattle, USA

4 Science for Life Laboratory, School of Biotechnology, KTH - Royal Institute of Technology, Solna, Sweden

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Citation and License

BMC Bioinformatics 2012, 13(Suppl 16):S3  doi:10.1186/1471-2105-13-S16-S3

Published: 5 November 2012


Peptides are routinely identified from mass spectrometry-based proteomics experiments by matching observed spectra to peptides derived from protein databases. The error rates of these identifications can be estimated by target-decoy analysis, which involves matching spectra to shuffled or reversed peptides. Besides estimating error rates, decoy searches can be used by semi-supervised machine learning algorithms to increase the number of confidently identified peptides. As for all machine learning algorithms, however, the results must be validated to avoid issues such as overfitting or biased learning, which would produce unreliable peptide identifications. Here, we discuss how the target-decoy method is employed in machine learning for shotgun proteomics, focusing on how the results can be validated by cross-validation, a frequently used validation scheme in machine learning. We also use simulated data to demonstrate the proposed cross-validation scheme's ability to detect overfitting.