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Compact bidirectional state-space autoencoders can outperform substantially larger dense autoencoders for joint cardiac transcriptomic classification while reducing parameters.
Missense variants can alter protein stability, conformation, activity, or expression, yet their clinical interpretation remains challenging, particularly for variants of uncertain significance. Protein language models provide sequence derived representations that capture evolutionary and biophysical constraints, but...
The findings demonstrate that convolutional, transformer-based, and hybrid deep learning architectures can be successfully trained on real microscopy data for species-level malaria diagnosis and support the feasibility of deploying scalable, automated diagnostic systems to improve both accuracy and accessibility of mal...
The study supports the potential usefulness of combining contact-based models with explainable graph neural networks for scenario-based epidemic analysis and suggests that the k-GCN model captures relevant temporal and structural dependencies in the simulated graph-organized data.
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