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Correcting mistakes in predicting distributions.

ABSTRACT: Motivation: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. Results: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. Availability and implementation:Datasets and a simple JavaScript tool available freely for all users at http://www.rostlab.org/services/distributions. Supplementary information:Supplementary data are available at Bioinformatics online.

SUBMITTER: Marot-Lassauzaie V

PROVIDER: S-EPMC6157078 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Correcting mistakes in predicting distributions.

Marot-Lassauzaie Valérie V Bernhofer Michael M Rost Burkhard B

Bioinformatics (Oxford, England) 20181001 19

<h4>Motivation</h4>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.<h4>Results</h4>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 po ...[more]

PMID: 29762646

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Correcting mistakes in predicting distributions.

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Correcting mistakes in predicting distributions.

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