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# sCellTransformer
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in single-cell RNA-seq and spatial transcriptomics data. It processes raw gene expression profiles across multiple cells to predict discretized
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gene expression levels for unseen cells without retraining. The model handles up to 20,000 protein-coding genes and outputs around a million
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gene expression tokens, mitigating the sparsity typical in single-cell datasets.
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# sCellTransformer
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sCellTransformer (sCT) is a long-range foundation model designed for zero-shot prediction tasks
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in single-cell RNA-seq and spatial transcriptomics data. It processes raw gene expression profiles across multiple cells to predict discretized
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gene expression levels for unseen cells without retraining. The model handles up to 20,000 protein-coding genes and outputs around a million
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gene expression tokens, mitigating the sparsity typical in single-cell datasets.
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