What it is
Tahoe-x1 is a family of perturbation-trained single-cell foundation models pretrained on large-scale transcriptomic datasets including Tahoe-100M. It jointly learns gene, cell and compound representations and is evaluated on cancer-relevant and perturbation-response tasks.
Evidence trail
BioAtlas keeps the path from source to decision visible. A connection records provenance; it does not imply that evidence is sufficient for every context.
Model passport
How Tahoe-x1 represents biology
Category is navigation. These fields describe the model-specific computational transformation and deliberately override broad category defaults.
Biological scale
Modalities & tasks
Registry, claims and frontier intelligence
Version history not yet curated
1 version record · release year not yet normalized. Model-family identity remains separate from capability and access changes.
Explore version lineage →1 normalized claim
Cell perturbation prediction · Four disease-relevant single-cell evaluation groups
Open claim intelligence →0 connected frontiers
No frontier-research record currently connects to this model.
Inspect research horizon →Inputs and outputs
Inputs
Single-cell expression profileDrug / perturbation contextOutputs
Cell representationsGene-essentiality predictionsPerturbation-response predictionsScientific and technical profile
Scientific principles
Technology
Scientific lineage
These are transparent concept matches—not claims that one scientist alone caused this model. Each connection is based on the model’s recorded domain, scientific principles, technical terms or an explicit lineage link.
Gene regulation and the operon model
François Jacob & Jacques MonodTarget biology, perturbation models, transcriptomic response prediction and virtual cells all require an explicit model of regulated gene programs.
Cooperative ligand binding
Archibald V. HillDose–response curves, receptor occupancy, multisite binding and systems pharmacology still use Hill-type models.
Information, entropy and communication
Claude E. ShannonSequence modelling, cross-entropy training, language models, mutual information and representation learning all use Shannon’s framework.
The epigenetic landscape and cell-fate trajectories
Conrad H. WaddingtonSingle-cell embeddings, trajectory inference, reprogramming and virtual-cell models often represent cell identity as movement through a learned state landscape.
The central dogma and directional information transfer
Francis CrickMulti-omic models and sequence foundation models connect genotype, transcript and protein through this information-flow framework.
Concerted allostery
Jacques Monod, Jeffries Wyman & Jean-Pierre ChangeuxAllosteric drug design exploits remote pockets to modulate function, selectivity and resistance without competing at the active site.
Evaluation evidence
Task-specific evidence only; not comparable as a universal leaderboard score.
Four disease-relevant single-cell evaluation groups
Version history not yet curated · Split details not yet normalizedA structured benchmark claim is recorded; consult the linked source for numeric values and protocol details.
Claim caveats
- Protocol, split and implementation details must match before comparing this claim with another result.
Known limitations
- Performance depends on the evaluation dataset and operating conditions.
- Task-specific benchmark results should not be compared across unlike domains.
- Outputs require task-specific scientific and experimental validation.
Milestones
The authors report state-of-the-art performance across four disease-relevant evaluation groups; independent reproduction should be tracked separately.