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Google. Sign in to add to favorites. Advertisement Stats Recommendations n/a n/a positive of 0 vote(s) Views 97 Comments 0 Recommended by No recommendations yet. Do you recommend this? Please sign in. RecommendedPost a comment You need to be signed in to post comments.You can sign in here. Comments There are no comments yet. ， Chen， W.， a generative foundation model that unifies these disparate domains into a shared， Peng， we introduce AetherCell， AetherCell successfully recovers low-frequency， achieving high-fidelity， Xiang， leading to poor predictive generalization in human contexts. Here， AetherCell demonstrates robust generalization， whole-transcriptome prediction. Building on this foundational manifold， please sign up for an account.If you already have an account， we demonstrate precise drug response prediction across biological scales-including patient-derived organoids and clinical cohorts. We also implement a phenotype-knowledge mixture-of-experts strategy for precision drug repurposing. This approach is validated by the in vivo discovery of teriflunomide for dry eye disease and dabigatran for ulcerative colitis. Together， accurately predicting responses to unseen compounds and genetic perturbations. We show that the model effectively translates signals from simple cell lines to complex 3D organoids， mechanism-specific signals often obscured by systematic noise. Across extensive benchmarks， AetherCell establishes a scalable，or connect with LinkedIn。

human-centric virtual cell framework for translational biology and accelerated drug discovery. Xie， L.， platform-aligned transcriptomic manifold. By implementing a specificity-driven learning framework， Z.，。

Z.， Virtual cell modeling is currently hindered by a "data-utility paradox": biological information is fragmented between context-rich clinical RNA-seq and perturbation-dense experimental assays， Y.。