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@@ -35,7 +35,7 @@ We built MIMIC to address these limitations. **MIMIC is a generative foundation
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## How MIMIC works
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MIMIC is a billion-parameter encoder-decoder transformer trained to jointly model molecular data of the central dogma. **Where most biological AI models take a *single* type of input and produce a *single* type of output, MIMIC is designed to take in *any available* molecular information is available and generate what is missing.** Protein surface geometry, RNA splice patterns, evolutionary conservation scores, and natural language descriptions of experimental conditions are all first-class inputs and outputs within the same model.
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MIMIC is a billion-parameter encoder-decoder transformer trained to jointly model molecular data of the central dogma. **Where most biological AI models take a *single* type of input and produce a *single* type of output, MIMIC is designed to take in *any available* molecular information and generate what is missing.** Protein surface geometry, RNA splice patterns, evolutionary conservation scores, and natural language descriptions of experimental conditions are all first-class inputs and outputs within the same model.
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### Open Source Resources
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We are currently preparing MIMIC code and weights for public release. We are also preparing LORE for public release, including data clustering, splits, and pre-computed tokenization. Upon release, both the MIMIC model and the LORE dataset will be available on the [Polymathic AI GitHub](https://github.com/PolymathicAI).
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We are currently preparing MIMIC code and weights for public release. We are also preparing LORE for public release, including data clustering, splits, and pre-computed tokenization. Upon release, both the MIMIC model and the LORE dataset will be available on the [Polymathic AI GitHub](https://github.com/PolymathicAI/MIMIC/).
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*-- Sophie Barstein, Samuel Sledzieski, Jake Kovalic*
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