arXiv:2606.07760v1 Announce Type: new Abstract: Understanding cellular phenotypes and how they respond to perturbations is critical for disease biology and therapeutic design. Single-cell RNA sequencing enables characterization at cellular resolution, yet the combinatorial space of conditions makes exhaustive experimental mapping infeasible. We introduce single-cell Concept Bottleneck Generative Models (scCBGM), a framework for interpretable and precise counterfactual editing of individual cells. scCBGM adapts concept bottleneck architectures for single-cell data through decoder skip connectio
The proliferation of single-cell sequencing technologies and advancements in generative AI models are converging to enable new methods for biological understanding.
This development offers a powerful tool for understanding complex cellular responses to perturbations, which is critical for accelerating drug discovery and therapeutic development.
The ability to precisely and interpretably 'edit' cellular phenotypes counterfactually via computational models significantly enhances the potential for targeted biological interventions, moving beyond exhaustive experimental mapping.
- · Biopharmaceutical companies
- · Synthetic biology researchers
- · Precision medicine developers
- · AI-driven drug discovery platforms
- · Traditional high-throughput screening methods
- · Companies reliant on extensive experimental validation
More efficient and targeted design of therapeutic compounds by predicting cellular responses.
Accelerated development of gene therapies and cell reprogramming techniques based on validated 'edits'.
Ethical and regulatory considerations around the precise computational control and modification of biological systems for human intervention.
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Read at arXiv cs.LG