ArticleBioinformatics (Oxford, England)2018
Correcting mistakes in predicting distributions.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Spectrum of Protein Location in Proteomes Captures Evolutionary Relationship Between Species.Journal of molecular evolution · 2021Article
- Detecting sequence signals in targeting peptides using deep learning.Life science alliance · 2019Article
- Detailed prediction of protein sub-nuclear localization.BMC bioinformatics · 2019Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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