ArticlePloS one2023
The role of data imbalance bias in the prediction of protein stability change upon mutation.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Bridging Algorithms and Biocatalysis: Perspectives on AI-Supported Enzyme Engineering.Molecules (Basel, Switzerland) · 2026Review
- Decoding the effects of mutation on protein interactions using machine learning.Biophysics reviews · 2025Review
- Structure-based self-supervised learning enables ultrafast protein stability prediction upon mutation.Innovation (Cambridge (Mass.)) · 2025Article
- Guiding questions to avoid data leakage in biological machine learning applications.Nature methods · 2024Review
- Deep learning in microbiome analysis: a comprehensive review of neural network models.Frontiers in microbiology · 2024Review
- Predicting thermostability difference between cellular protein orthologs.Bioinformatics (Oxford, England) · 2023Article
- Predicting the Effect of Single Mutations on Protein Stability and Binding with Respect to Types of Mutations.International journal of molecular sciences · 2023Article
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Authors and funding
1 author.
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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.
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Registered trials
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