A cross-validation scheme for machine learning algorithms in shotgun proteomics
2012 (English)In: BMC Bioinformatics, ISSN 1471-2105, Vol. 13, S3- p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2012. Vol. 13, S3- p.
Tandem Mass-Spectrometry, False Discovery Rate, Peptide Identification, Statistical Significance, Protein Identifications, Database Search, Spectra, Model, Probabilities, Networks
IdentifiersURN: urn:nbn:se:kth:diva-116737DOI: 10.1186/1471-2105-13-S16-S3ISI: 000312714500003OAI: oai:DiVA.org:kth-116737DiVA: diva2:600697
FunderSwedish Research CouncilSwedish Foundation for Strategic Research Science for Life Laboratory - a national resource center for high-throughput molecular bioscience
QC 201301252013-01-252013-01-252013-01-25Bibliographically approved