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Deciphering subcellular organization with multiplexed imaging and deep learning
Our study introduces conditional autoencoder for multiplexed pixel analysis (CAMPA), a deep-learning framework that uses highly multiplexed imaging to identify consistent subcellular landmarks across heterogeneous cell populations and experimental perturbations. Generating interpretable cellular phenotypes revealed links between subcellular organization and perturbations of RNA production, RNA processing and cell size.
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Method of the Year 2021: Protein structure prediction
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Machine learning in rare disease
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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets
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SIMBA: single-cell embedding along with features
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Simultaneous profiling of spatial gene expression and chromatin accessibility during mouse brain development