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Harvard University
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Partial data augmentation can match the statistical benefits of full augmentation, challenging the notion that complete symmetry is necessary for optimal learning.
GNNs can significantly improve heterogeneous cryo-EM reconstruction by leveraging protein structure priors, outperforming MLPs in predicting atomic backbone conformations.
