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  • 1.
    Afkham, Heydar Maboudi
    et al.
    KTH, School of Computer Science and Communication (CSC).
    Qiu, Xuanbin
    KTH, School of Computer Science and Communication (CSC).
    The, Matthew
    KTH, School of Biotechnology (BIO), Gene Technology. KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Käll, Lukas
    KTH, Centres, Science for Life Laboratory, SciLifeLab.
    Uncertainty estimation of predictions of peptides' chromatographic retention times in shotgun proteomics2017In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 33, no 4, p. 508-513Article in journal (Refereed)
    Abstract [en]

    Motivation: Liquid chromatography is frequently used as a means to reduce the complexity of peptide-mixtures in shotgun proteomics. For such systems, the time when a peptide is released from a chromatography column and registered in the mass spectrometer is referred to as the peptide's retention time. Using heuristics or machine learning techniques, previous studies have demonstrated that it is possible to predict the retention time of a peptide from its amino acid sequence. In this paper, we are applying Gaussian Process Regression to the feature representation of a previously described predictor ELUDE. Using this framework, we demonstrate that it is possible to estimate the uncertainty of the prediction made by the model. Here we show how this uncertainty relates to the actual error of the prediction. Results: In our experiments, we observe a strong correlation between the estimated uncertainty provided by Gaussian Process Regression and the actual prediction error. This relation provides us with new means for assessment of the predictions. We demonstrate how a subset of the peptides can be selected with lower prediction error compared to the whole set. We also demonstrate how such predicted standard deviations can be used for designing adaptive windowing strategies.

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