Deviation Error: assessing machine learning predictions for replicate measurements in genomics and beyond

Abdulnabi, H.; Westwood, J. T.

doi:10.1101/2025.05.29.656931

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bioRxiv · 10.1101/2025.05.29.656931

Deviation Error: assessing machine learning predictions for replicate measurements in genomics and beyond

Abdulnabi, H.·Westwood, J. T.

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Abstract

Withdrawal StatementThe authors have withdrawn this manuscript because the foundational metric used throughout the study, the Deviation Error, requires substantial mathematical formalization to be established as a proper scoring rule. Completing this rigorous theoretical validation and the necessary overhaul of the accompanying synthetic case studies requires significant additional research. We intend to revisit the Deviation Error methodology in future research. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

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Abdulnabi, H., Westwood, J. T.. 2025-06-02. Deviation Error: assessing machine learning predictions for replicate measurements in genomics and beyond. https://doi.org/10.1101/2025.05.29.656931

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