MIMIC is a split-track encoder-decoder foundation model that unifies sequence reconstruction, prediction, and constrained design across nucleic acids, proteins, and regulatory context using partially observed multimodal inputs.
Protst: Multi-modality learning of protein sequences and biomedical texts
2 Pith papers cite this work. Polarity classification is still indexing.
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SubQuad delivers near-subquadratic retrieval and equity-aware clustering for adaptive immune repertoires, achieving faster throughput and memory use while maintaining recall and subgroup balance on viral and tumor data.
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MIMIC: A Generative Multimodal Foundation Model for Biomolecules
MIMIC is a split-track encoder-decoder foundation model that unifies sequence reconstruction, prediction, and constrained design across nucleic acids, proteins, and regulatory context using partially observed multimodal inputs.
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SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework
SubQuad delivers near-subquadratic retrieval and equity-aware clustering for adaptive immune repertoires, achieving faster throughput and memory use while maintaining recall and subgroup balance on viral and tumor data.