We envision" AI scientists" as systems capable of skeptical learning and reasoning that
empower biomedical research through collaborative agents that integrate AI models and …

Cells are essential to understanding health and disease, yet traditional models fall short of
modeling and simulating their function and behavior. Advances in AI and omics offer …

Cell states were traditionally defined by how they looked, where they were located, and what
functions they performed. In this post-genomic era, the field is largely focused on a …

The growth of artificial intelligence (AI) has led to an increase in the adoption of computer
vision and deep learning (DL) techniques for the evaluation of microscopy images and …

Biological cells rely on precise spatiotemporal coordination of biochemical reactions to
control their functions. Such cell signaling networks have been a common focus for …

Functional cell processes (eg, molecular signaling, response to stimuli, mitosis, etc.) impact
cell phenotypes, which scientists can measure with cell morphology. However, linking these …

Biological cells rely on precise spatiotemporal coordination of biochemical reactions to
control their many functions. Such cell signaling networks have been a common focus for …

The success of artificial intelligence (AI) algorithms in predicting protein structure and more
recently, protein interactions, demonstrates the power and potential of machine learning and …

Organ function requires coordinated activities of thousands of genes in distinct, spatially
organized cell types. Understanding the basis of emergent tissue function requires …

Building a virtual cell capable of accurately simulating cellular behaviors in silico has long
been a dream in computational biology. We introduce CellFlow, an image-generative model …