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This paper introduces CoDiffGRN, a co-evolutionary discrete diffusion framework for gene regulatory network inference, along with a new benchmark BEELINE-KGC for inductive evaluation. It achieves state-of-the-art performance in novel regulatory discovery.
SpaCellAgent is a self-evolving LLM-based multi-agent framework that automates end-to-end spatiotemporal trajectory analysis from natural language queries, achieving over 40% improvement in analytical efficiency while maintaining expert-level performance.
PerturbCellRL introduces a reinforcement learning framework that post-trains a pretrained single-cell transcriptomic generator using cell-level verifiers as rewards, improving biological consistency of perturbation predictions beyond distributional matching.
This paper introduces PolyILR, a canonical orthonormal decomposition of the Aitchison tangent space that aligns with any tree topology, providing stable and interpretable features for compositional data such as microbiome and single-cell profiles.
The article introduces FreeBridge, a variational Schrödinger Bridge framework for modeling cellular transitions from high-content imaging data, learning stochastic transport constrained within a fixed cellular manifold to achieve competitive endpoint fidelity and reduce intermediate support violations.
CellBRIDGE is a new method that enhances optimal transport for scRNA-seq trajectory inference by incorporating ligand-receptor interaction costs to model cell-cell communication, improving alignment and enabling interpretable in silico perturbations.
PerturbSpace is a spatial transcriptomics method that presses a tissue section onto a chip of barcoded microwells, using antibodies to tag cells with location codes before single-cell sequencing, achieving >90% confident spatial assignment.
A new preprint from the Arc Institute introduces PerturbSpace, a method for spatially resolved, multimodal whole-transcriptome CRISPR screens compatible with standard single-cell workflows.
Introduces scShapeBench, a benchmark dataset for shape detection in high-dimensional single-cell data, and scReebTower, a baseline method that uses diffusion geometry and Reeb graphs to classify data shapes into clusters, trajectories, multi-branches, and archetypes.
This paper introduces GATHER, a convergence-centric retrieval method for zero-shot cell-type annotation using knowledge graphs, which improves accuracy and reduces LLM costs compared to existing KG-RAG baselines.
This paper introduces Shesha, a geometric stability metric that quantifies directional coherence of single-cell CRISPR perturbation responses using mean cosine similarity, revealing regulatory architecture and predicting cellular stress across 2,200+ perturbations in five CRISPR datasets.