The role of data imbalance bias in the prediction of protein stability change upon mutation

Jianwen Fang

PLOS ONE, 2023, vol. 18, issue 3, 1-10

Abstract: There is a controversy over what causes the low robustness of some programs for predicting protein stability change upon mutation. Some researchers suggested that low-quality data and insufficiently informative features are the primary reasons, while others attributed the problem largely to a bias caused by data imbalance as there are more destabilizing mutations than stabilizing ones. In this study, a simple approach was developed to construct a balanced dataset that was then conjugated with a leave-one-protein-out approach to illustrate that the bias may not be the primary reason for poor performance. A balanced dataset with some seemly good conventional n-fold CV results should not be used as a proof that a model for predicting protein stability change upon mutations is robust. Thus, some of the existing algorithms need to be re-examined before any practical applications. Also, more emphasis should be put on obtaining high quality and quantity of data and features in future research.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0283727

DOI: 10.1371/journal.pone.0283727

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