Research - CZ Biohub
webCZ Biohub is a Chan Zuckerberg Initiative research institute; this page is tangentially relevant to AI safety as a showcase of advancing biological AI capabilities, including LLM scientific reasoning and virtual cell models.
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Summary
The Chan Zuckerberg Biohub research page aggregates publications from its teams and partners, covering cutting-edge work in AI-driven biology including foundation models for single-cell transcriptomics, virtual cell construction, and biological reasoning in LLMs. The organization promotes open science through preprints and has supported over 8,000 publications since 2015. Recent work includes AI models for cellular regulation, cross-species generative cell atlases, and guidance methods for diffusion models.
Key Points
- •Hosts publications spanning AI/ML applied to biology: foundation models (GREmLN, TranscriptFormer), virtual cell AI, and scientific reasoning LLMs
- •Includes 'How to build the virtual cell with artificial intelligence' (Cell, 2024) — a priority roadmap for AI-driven biology
- •Features rbio1, a project training LLMs with biological world models as soft verifiers for scientific reasoning
- •Promotes open-access science via preprint deposition; 8,000+ publications supported since 2015
- •Relevant to AI capabilities research: demonstrates rapid advancement of biological AI foundation models and generative approaches
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| Chan Zuckerberg Initiative | Organization | 50.0 |
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Scientific research publications - Biohub
Research
We believe in sharing the research findings of our teams and partners openly to accelerate understanding of human health and disease. We strongly encourage researchers to deposit manuscripts as preprints before peer review to increase access to research findings and to communicate results more quickly. Since 2015, we have supported more than 8,000 publications.
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Imaging
Immune cell reprogramming
Technology
Tissue instrumentation & inflammation
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March 11, 2026
Amino acid supplementation enhances in vivo efficacy of lipid nanoparticle-mediated mRNA delivery in preclinical models
Kangfu Chen, Wenhan Wang, Amber Lennon, et al. (2026) | Science Translational Medicine
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March 5, 2026
Tissue-specific clonal selection and differentiation of CD4⁺ T cells during infection
Roham Parsa, Arpita Sushil (2026) | Nature Immunology
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February 28, 2026
AI-Guided CRISPR Screen Accelerates Discovery of New Drug Targets
Mushaine Shih, Amber Lennon, Jason Perera, et al. (2026) | bioRxiv
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February 9, 2026
Virtual Cells Need Context, Not Just Scale
Payam Dibaeinia, Sudarshan Babu, Mei Knudson, et al. (2026) | bioRxiv
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February 9, 2026
DecoderTCR: Compositional Pretraining and Entropy-Guided Decoding for TCR-pMHC Interactions
Ben Lai, Melissa Englund, Ramit Bharanikumar, et al. (2026) | bioRxiv
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November 4, 2025
Scalable Single-Cell Gene Expression Generation with Latent Diffusion Models
Giovanni Palla, Sudarshan Babu, Payam Dibaeinia, et al. (2025) | arXiv
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November 2, 2025
VariantFormer: A hierarchical transformer integrating DNA sequences with genetic variations and regulatory landscapes for personalized gene expression prediction
Sayan Ghosal, Youssef Barhomi, Tejaswini Ganapathi, et al. (2025) | bioRxiv
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October 10, 2025
A path towards AI-scale, interoperable biological data
Brian Aevermann, Andrea Califano, Chi-Li Chiu, et al. (2025) | arXiv
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August 28, 2025
Tissue-specific clonal selection and differentiation of CD4⁺ T cells during infection
Roham Parsa, Helder Assis, Tiago B.R. de Castro, et al. (2025) | bioRxiv
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