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README.md
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metrics:
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# CELL-E 2
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## Model description
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CELL-E 2 is the second iteration of the original [CELL-E](https://www.biorxiv.org/content/10.1101/2022.05.27.493774v1) model which utilizes an amino acid sequence and nucleus image to make predictions of subcellular protein localization with respect to the nucleus.
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We trained on the [Human Protein Atlas](https://www.proteinatlas.org) (HPA) and the [OpenCell](https://opencell.czbiohub.org) datasets.
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CELL-E 2 utilizes pretrained amino acid embeddings from [ESM-2](https://github.com/facebookresearch/esm).
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## Model variations
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metrics:
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- accuracy
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[](huanglab.ucsf.edu)
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# CELL-E 2
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[](https://github.com/BoHuangLab/CELL-E_2)
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## Model description
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CELL-E 2 is the second iteration of the original [CELL-E](https://www.biorxiv.org/content/10.1101/2022.05.27.493774v1) model which utilizes an amino acid sequence and nucleus image to make predictions of subcellular protein localization with respect to the nucleus.
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We trained on the [Human Protein Atlas](https://www.proteinatlas.org) (HPA) and the [OpenCell](https://opencell.czbiohub.org) datasets.
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CELL-E 2 utilizes pretrained amino acid embeddings from [ESM-2](https://github.com/facebookresearch/esm).
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Localization is predicted as a binary image atop the provided nucleus. The logit values are weighted against these binary images to produce a heatmap of expected localization.
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## Model variations
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