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*In Bioinformatics and biology insights *

**Background** :

**Results** : *R* ^{2}) for regression models when tested on the holdout dataset. This upper bound depends only on the noise associated with the response variable in a dataset as well as its variance. The upper bound estimate was validated via Monte Carlo simulations and then used as a tool to bootstrap performance of regression models trained on biological datasets, including protein sequence data, transcriptomic data, and genomic data.

**Conclusions** :

*Li Gang, Zrimec Jan, Ji Boyang, Geng Jun, Larsbrink Johan, Zelezniak Aleksej, Nielsen Jens, Engqvist Martin Km*

*2021*

**experiment noise, label noise, machine learning, regression models, upper bound**

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