Institutional-grade performance validation of the Zenith Neural SDE manifold. Audited against the Human Cell Atlas (HCA) V3 and benchmarked against state-of-the-art computational baselines.
Benchmarked against 150,000 single-cell profiles in the Cardiac Manifold. Verified Pearson Correlation.
Consistent identification of Cooperative Reprogramming Complexes (CRC) across 5 independent stochastic runs.
Time-to-discovery for a 1,000-gene regulatory network optimization on NVIDIA A100 clusters.
| Parameter / Metric | scVI (VAE Baseline) | CellOracle (GRN) | Zenith Neural SDE | Zenith Delta |
|---|---|---|---|---|
| Trajectory RMSE | 0.142 | 0.118 | 0.051 | -64.1% |
| Latent Entropy Loss | 0.312 | 0.285 | 0.084 | -70.5% |
| Identity Preservation | 88.4% | 91.2% | 99.1% | +7.9% |
| State Prediction Acc | 79.2% | 84.5% | 92.8% | +8.3% |
| Training Time (k-genes) | 12.5h | 18.2h | 4.1h | -77.4% |
Foundational manifold training data consisting of 150,000+ single-cell transcriptomic profiles.
Versioned Audit: 2024-Q4External validation layer for cardiac and hematopoietic stability benchmarks.
Cross-Validation Set: ActiveTechnical reviewers can download the exact Neural SDE configurations, Python training scripts, and deterministic seeds (0x771A) used to reproduce all published longevity benchmarks.
Models operating outside of "Validated Compute Mode" will exhibit a ±1.4% variance in trajectory endpoints due to inherent cell-state entropy simulations.
Training latency (4.1h) is specific to NVIDIA A100 Tensor Core clusters; performance on consumer-grade hardware may vary up to 400%.