CisTransCell: Single-Cell Perturbation Prediction via Gene Function, Regulatory Control, and Cellular Context
arXiv:2606.13713v1 Announce Type: cross Abstract: Predicting cellular transcriptional responses to genetic perturbations is a central problem in single-cell biology, especially in the zero-shot setting where the perturbed gene or gene combination is unseen during training. A major difficulty is that perturbation effects are not determined by expression state alone: they depend on how the perturbed gene product influences other genes and proteins, how those downstream factors act on cis-regulatory elements, and which regulatory programs are active in the current cell state. To better capture th
The accelerating pace of AI development combined with the increasing datasets in single-cell biology and gene editing tools creates a natural convergence point for advanced predictive models.
Predicting cellular responses to perturbations is crucial for developing new therapeutics, engineering biological systems, and understanding complex diseases, moving synthetic biology closer to precise programmability.
The ability to more accurately predict zero-shot genetic perturbation outcomes will significantly reduce experimental costs and accelerate drug discovery and synthetic biology design cycles.
- · Biopharmaceutical companies
- · Synthetic biology startups
- · AI-driven drug discovery platforms
- · CRISPR gene editing developers
- · Traditional drug screening methods
- · Labs relying solely on exhaustive experimental validation
Faster and cheaper development of targeted therapies and engineered biological systems.
Reduced development timelines could lead to more accessible and personalized medicine options.
The ethical and regulatory frameworks around programmable biology will need to evolve rapidly to keep pace with these predictive capabilities.
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Read at arXiv cs.AI