OmicsLM integrates continuous omics embeddings into LLMs for multi-sample biological reasoning, matching specialized models on profile tasks while outperforming them and general LLMs on language-guided QA over real expression data.
Universal Cell Embeddings: A Foundation Model for Cell Biology
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Shesha quantifies directional coherence of single-cell CRISPR responses, correlates strongly with effect magnitude, distinguishes pleiotropic from lineage-specific regulators, and predicts chaperone activation after magnitude correction.
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OmicsLM: A Multimodal Large Language Model for Multi-Sample Omics Reasoning
OmicsLM integrates continuous omics embeddings into LLMs for multi-sample biological reasoning, matching specialized models on profile tasks while outperforming them and general LLMs on language-guided QA over real expression data.
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Geometric coherence of single-cell CRISPR perturbations reveals regulatory architecture and predicts cellular stress
Shesha quantifies directional coherence of single-cell CRISPR responses, correlates strongly with effect magnitude, distinguishes pleiotropic from lineage-specific regulators, and predicts chaperone activation after magnitude correction.