scCycleMol adds a learnable circular cell-cycle head with closed-loop supervision from predicted treated expression, yielding higher r-squared on SciPlex3 gene predictions and improved phase accuracy versus ChemCPA baselines.
Nature Methods , volume=
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Chem2Gen-Bench is a new benchmark and evaluation framework for measuring alignment between chemical and genetic perturbation responses in matched cell-target contexts using retrieval, similarity, and embedding comparisons.
OCOO-T is a flow-matching Transformer model that directly denoises continuous gene expression profiles to predict transcriptional responses to perturbations and reports state-of-the-art results on Tahoe100M, Replogle, and PBMC benchmarks.
Neuro-JEPA is a sparse multimodal foundation model pretrained on 1,551,862 brain MRI scans that shows stronger and more consistent performance than existing models and CNN baselines across 47 tasks from clinical and public datasets.
K-nearest neighbor from a knowledge graph beats most methods on out-of-distribution transcriptomic perturbation prediction, and an RL-trained reasoning LLM matches SOTA on Replogle et al. (2022) cell lines while improving downstream differential expression prediction.
Two new methods distill implicit regulatory knowledge from single-cell foundation models to enable generalizable gene regulatory network inference on unseen data.
citing papers explorer
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Modeling Cell-Cycle-Aware Single-Cell Drug Perturbation Responses
scCycleMol adds a learnable circular cell-cycle head with closed-loop supervision from predicted treated expression, yielding higher r-squared on SciPlex3 gene predictions and improved phase accuracy versus ChemCPA baselines.
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Chem2Gen-Bench: Benchmarking Chemical-to-Genetic Translation in Perturbation Response Space
Chem2Gen-Bench is a new benchmark and evaluation framework for measuring alignment between chemical and genetic perturbation responses in matched cell-target contexts using retrieval, similarity, and embedding comparisons.
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OCOO-T : A Simple and Scalable Virtual Cell Model for Transcriptional Perturbation Response Prediction
OCOO-T is a flow-matching Transformer model that directly denoises continuous gene expression profiles to predict transcriptional responses to perturbations and reports state-of-the-art results on Tahoe100M, Replogle, and PBMC benchmarks.
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Learning Sparse Latent Predictive Foundation Model for Multimodal Neuroimaging
Neuro-JEPA is a sparse multimodal foundation model pretrained on 1,551,862 brain MRI scans that shows stronger and more consistent performance than existing models and CNN baselines across 47 tasks from clinical and public datasets.
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Knowledge Graphs and Reasoning LLMs for Finding Simple Yet Effective Transcriptomic Perturbation Predictors
K-nearest neighbor from a knowledge graph beats most methods on out-of-distribution transcriptomic perturbation prediction, and an RL-trained reasoning LLM matches SOTA on Replogle et al. (2022) cell lines while improving downstream differential expression prediction.
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Towards Universal Gene Regulatory Network Inference: Unlocking Generalizable Regulatory Knowledge in Single-cell Foundation Models
Two new methods distill implicit regulatory knowledge from single-cell foundation models to enable generalizable gene regulatory network inference on unseen data.