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How to build the virtual cell with artificial intelligence: Priorities and opportunities.
Cell
December 2024
Chan Zuckerberg Initiative, Redwood City, CA, USA; Department of Bioengineering, Stanford University, Stanford, CA, USA; Department of Applied Physics, Stanford University, Stanford, CA, USA. Electronic address:
Article Synopsis
- Cells are crucial for studying health and diseases, but traditional models are limited in their ability to accurately represent cell function and behavior.
- Advances in AI and omics technology enable the development of AI virtual cells (AIVCs), complex models that simulate molecular, cellular, and tissue behavior across various conditions.
- The creation of AIVCs aims to enhance biological research by allowing detailed simulations, speeding up discoveries, and promoting collaborative and interdisciplinary approaches in open scientific research.
Learning the language of DNA.
Science
November 2024
Gladstone Institute of Cardiovascular Disease, San Francisco, CA, USA.
A genomic foundation model broadly enables sequence modeling, prediction, and design.
View Article and Find Full Text PDFHow to Build the Virtual Cell with Artificial Intelligence: Priorities and Opportunities.
ArXiv
October 2024
Chan Zuckerberg Initiative, Redwood City, CA, USA.
The cell is arguably the most fundamental unit of life and is central to understanding biology. Accurate modeling of cells is important for this understanding as well as for determining the root causes of disease. Recent advances in artificial intelligence (AI), combined with the ability to generate large-scale experimental data, present novel opportunities to model cells.
View Article and Find Full Text PDFWhile often represented as static entities, gene networks are highly context-dependent. Here, we developed a multi-task learning strategy to yield context-specific representations of gene network dynamics. We assembled a corpus comprising ~103 million human single-cell transcriptomes from a broad range of tissues and diseases and performed a two stage pretraining, first with non-malignant cells to generate a foundational model and then with continual learning on cancer cells to tune the model to the cancer domain.
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