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AI drug discovery models/Spatial & Tissue Systems/AIDO.Tissue
model-family passport · Review date not recorded

AIDO.Tissue

Spatially informed foundation modelling of cells in tissue neighborhoods.

4/7Evidence fields documented
60-SECOND EVALUATION VIEW

What should a scientist know before using AIDO.Tissue?

SupportedEvidence supports the stated context with explicit boundaries
Best suited forRepresentation · Prediction
Evidence supportsSpatial transcriptomics downstream evaluations: Preprint / open evaluation
Evidence does not establishUniversal superiority, therapeutic success, clinical utility or regulatory acceptance.
Major limitationPerformance depends on the evaluation dataset and operating conditions.
CompareAdd to evaluationView evidence
Current registry recordVersion history not yet curated1 recorded release · Review date not recorded. A newer version is not assumed to be universally better.

What it is

AIDO.Tissue is a spatial transcriptomics foundation-model family that represents a center cell together with neighboring cells so tissue context and cross-cell dependencies are retained during pretraining and downstream prediction.

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.

Sources4 connectedPrimary resources and normalized claims
Claims1 normalizedIntegrated discovery platform
EntityAIDO.Tissuemodel-family · Version history not yet curated
ReviewReview date not recordedReview date not claimed
ConclusionContext requiredAdd to an evaluation before operational use

Model passport

Entity typemodel-family
OrganizationGenBio AI
Model family introduced2026
AccessOpen source
Commercial useAllowed / verify checkpoint terms
DeploymentSelf-hosted
ComputeGPU recommended
Domainsspatial
Biology → representation → computation → evidence

How AIDO.Tissue represents biology

model-familyspatial

Category is navigation. These fields describe the model-specific computational transformation and deliberately override broad category defaults.

1 · Biological inputs
Center-cell expressionNeighbor-cell expressionSpatial neighborhood context
2 · Input representation
Gene-expression vectorsCell-neighborhood sequences2D positional encoding
3 · Internal representation
Spatial cell and neighborhood embeddings
4 · Architecture
Asymmetric encoder-decoder spatial foundation model
5 · Learning objective
Spatially guided pretrainingCross-cell dependency learning
6 · Output representation
Dense vectorsLabels / scores

Biological scale

celltissueneighborhood

Modalities & tasks

Spatial transcriptomicsSingle-cell transcriptomicsRepresentationPredictionSpatial context modelling

Registry, claims and frontier intelligence

Versioned registry

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 →
Benchmark claim ledger

1 normalized claim

Integrated discovery platform · Spatial transcriptomics downstream evaluations

Open claim intelligence →

Inputs and outputs

Inputs

Center-cell expressionNeighbor-cell expressionSpatial neighborhood context

Outputs

Cell embeddingsNiche / tissue-context predictionsCell-level regression or classification outputs

Scientific and technical profile

Scientific principles

Spatial contextNeighborhood-aware cell modelling

Technology

AIDO.Tissue-60MSpatial transcriptomics

Evaluation evidence

Dataset or evaluationSpatial transcriptomics downstream evaluations
Task or metricCell/tissue representation and prediction
Evidence statusPreprint / open evaluation
Open source ↗

Task-specific evidence only; not comparable as a universal leaderboard score.

Integrated discovery platform

Spatial transcriptomics downstream evaluations

Version history not yet curated · Split details not yet normalized
developer-reported

A 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.
Open benchmark claim →

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

2026

Public AIDO.Tissue checkpoints and downstream examples are available.