Correcting mistakes in predicting distributions

Bioinformatics
Valérie Marot-LassauzaieBurkhard Rost

Abstract

Many applications monitor predictions of a whole range of features for biological datasets, e.g. the fraction of secreted human proteins in the human proteome. Results and error estimates are typically derived from publications. Here, we present a simple, alternative approximation that uses performance estimates of methods to error-correct the predicted distributions. This approximation uses the confusion matrix (TP true positives, TN true negatives, FP false positives and FN false negatives) describing the performance of the prediction tool for correction. As proof-of-principle, the correction was applied to a two-class (membrane/not) and to a seven-class (localization) prediction. Datasets and a simple JavaScript tool available freely for all users at http://www.rostlab.org/services/distributions. Supplementary data are available at Bioinformatics online.

References

Nov 21, 2002·Protein Science : a Publication of the Protein Society·Chien Peter ChenBurkhard Rost
Sep 11, 2012·Bioinformatics·Tatyana GoldbergBurkhard Rost
Dec 30, 2014·Proteins·Jonas ReebBurkhard Rost
Feb 6, 2017·Methods in Molecular Biology·Sangya PundirClaire O'Donovan
May 13, 2017·Science·Peter J ThulEmma Lundberg

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Citations

Apr 25, 2019·BMC Bioinformatics·Maria LittmannBurkhard Rost
Oct 2, 2019·Life Science Alliance·Jose Juan Almagro ArmenterosHenrik Nielsen
Jul 31, 2021·Journal of Molecular Evolution·Valérie Marot-LassauzaieBurkhard Rost

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