arXiv:2606.08493v1 Announce Type: cross Abstract: \textit{Tissue graph counterfactuals} ask how a cell's expression would change under altered spatial neighbor contexts. Such queries are central to predicting cell behavior in tissues, but lack a unified definition, with existing methods targeting specific intervention types or treating cells as i.i.d. In this work, we first formalize \textit{tissue graph counterfactuals} as a class of spatial interventions that either rewire connections between cells (\textit{edge perturbation}) or modify the expression of their neighbors (\textit{node perturb
The increasing sophistication of AI and graph neural networks is enabling new approaches to model complex biological systems, pushing the boundaries of what's possible in computational biology.
This research provides a formal framework for predicting cellular behavior under various spatial conditions, which is crucial for understanding disease progression and designing targeted therapies.
The ability to formally query and predict counterfactuals on tissue graphs can accelerate drug discovery, personalized medicine, and the development of synthetic tissues.
- · Biotech companies
- · Pharmaceutical research
- · Computational biologists
- · AI/ML researchers in life sciences
- · Traditional assay methods reliant on extensive in-vitro/in-vivo trials
Improved understanding of disease mechanisms and cellular response to interventions.
Faster development and optimization of new therapeutic drugs and regenerative medicine approaches.
The creation of wholly synthetic, functional tissues designed with precise cellular interactions using AI-driven prediction.
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Read at arXiv cs.LG