arXiv:2607. 17671v1 Announce Type: new Abstract: Large-scale single-cell perturbation atlases make it possible to ask an inverse question: given an observed transcriptional response, which annotated targets and compounds in a fixed library are most consistent with that response?
arXiv:2610.15117v1 Announce Type: cross Abstract: To evaluate whether a compact bidirectional state-space autoencoder improves joint classification of species, cell type, and disease state from singl...
arXiv:2606. 09558v1 Announce Type: cross Abstract: Motivation: Transformer-based models are increasingly applied to large-scale single-cell transcriptomics, showing strong performance through self-supervised learning on millions of cells.
scDEFT is a deep learning framework that treats a drug as a conditioning operator on single‑cell representations, enabling prediction of drug‑induced state changes and responder status. The model learns drug‑conditioned cell latents via feature‑wise linear modulation, aggregates them over transcriptional neighborhoods, and ranks latent dimensions to identify genes distinguishing responders from non‑responders. Applied to a harmonized inflammatory bowel disease atlas of 1.16 million cells, scDEFT achieves 45% of the baseline‑to‑reproducibility ceiling in state‑change prediction and stratifies responders before treatment with an AUROC of 0.70, outperforming standard predictors.
The study evaluates the robustness of deep learning models for DNA regulatory sequence prediction under biologically and technically induced distribution shifts. Using a mechanistic simulation benchmark and a massively parallel reporter assay dataset, the authors show that while models perform well under mild GC content changes, they exhibit higher error, miscalibration, and coverage collapse when faced with motif rewiring, noise, and batch effects. Incorporating biological priors and uncertainty-aware selective prediction improves robustness, though protection against strong assay noise remains limited.
Accurate prediction of patient-specific therapeutic response from pre-treatment transcriptomes is hindered by the scarcity of matched clinical response labels and post-treatment molecular profiles. Preclinical transfer-learning models can simulate drug-induced expression changes but are often hard to interpret and unstable, whereas knowledge-graph methods provide mechanistic context yet remain static and fail to capture drug-induced transcriptomic perturbation dynamics.
arXiv:2510.11726v3 Announce Type: replace-cross Abstract: Generating drug-induced transcriptional responses requires a model to account for cellular background together with molecular identity and do...
arXiv:2606. 08973v1 Announce Type: cross Abstract: Fundamental investigations into how different molecular encoding methods affect molecular property prediction remain relatively limited.
arXiv:2606. 30695v1 Announce Type: cross Abstract: Single-cell drug perturbation models should predict not only transcriptional response magnitude, but also whether a treatment alters the proliferative state of a cell.
arXiv:2607. 04557v1 Announce Type: cross Abstract: Accurate prediction of patient-specific therapeutic response from pre-treatment transcriptomes is hindered by the scarcity of matched clinical response labels and post-treatment molecular profiles.
VINCENT is a post‑training framework that provides validated, chemically coherent explanations for drug synergy predictions by extracting atom‑pair evidence from attention and gradient signals, grouping them into motifs, and refining these motifs through repeated local perturbations. On a literature‑annotated subset of 25 drug pairs, VINCENT achieves a mean motif recall of 0.826, outperforming baselines (0.49–0.66). Across 71 test pairs, its validated interaction scores yield a TP/TN separation of 3.36, indicating more accurate recovery of literature‑supported molecular regions and better alignment with predictor behavior.
CellCanon is an agentic cellular world model that integrates single‑cell expression prediction, evidence‑grounded explanation, and observation‑driven updating. Its predictive core reuses condition‑level biological evidence to generate gene‑specific readouts, while a shared world state links predictions, measurements, and source‑linked claims to support explanation and revision. On 339 held‑out SciPlex3 conditions across three cell lines, CellCanon outperforms baselines in differential‑expression AUPRC, Macro‑F1, and direction Macro‑F1, and its explanation module correctly answers all answerable questions with supporting proofs.