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- W4385567764 abstract "This paper presents ExplainableFold (xFold), which is an Explainable AI framework for protein structure prediction. Despite the success of AI-based methods such as AlphaFold (αFold) in this field, the underlying reasons for their predictions remain unclear due to the black-box nature of deep learning models. To address this, we propose a counterfactual learning framework inspired by biological principles to generate counterfactual explanations for protein structure prediction, enabling a dry-lab experimentation approach. Our experimental results demonstrate the ability of ExplainableFold to generate high-quality explanations for AlphaFold's predictions, providing near-experimental understanding of the effects of amino acids on 3D protein structure. This framework has the potential to facilitate a deeper understanding of protein structures. Source code and data of the ExplainableFold project are available at https://github.com/rutgerswiselab/ExplainableFold." @default.
- W4385567764 created "2023-08-05" @default.
- W4385567764 creator A5016323755 @default.
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- W4385567764 date "2023-08-04" @default.
- W4385567764 modified "2023-09-27" @default.
- W4385567764 title "ExplainableFold: Understanding AlphaFold Prediction with Explainable AI" @default.
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- W4385567764 doi "https://doi.org/10.1145/3580305.3599337" @default.
- W4385567764 hasPublicationYear "2023" @default.
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