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AI drug discovery models/RNA Models & Design/UNI-RNA
model-family passport · Review date not recorded

UNI-RNA

A 400M-scale encoder foundation model for RNA representation.

1/7Evidence fields documented
60-SECOND EVALUATION VIEW

What should a scientist know before using UNI-RNA?

UnresolvedEvidence direction is incomplete or not yet resolved
Best suited forRepresentation · Prediction
Evidence supportsPrimary links may be present, but BioAtlas does not claim a review date without a record-level timestamp.
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

UNI-RNA is an encoder-only RNA foundation model trained with masked-language modelling for transferable RNA sequence representations and downstream 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.

Sources1 connectedPrimary resources and normalized claims
Claims0 normalizedNo normalized claim yet
EntityUNI-RNAmodel-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
OrganizationAcademic RNA foundation-model collaborators
Model family introducedNot normalized
AccessLimited open access
Commercial useAllowed / verify checkpoint terms
DeploymentHybrid
ComputeGPU recommended
Domainsrna
Biology → representation → computation → evidence

How UNI-RNA represents biology

model-familyrna

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

1 · Biological inputs
RNA sequence
2 · Input representation
Nucleotide tokens
3 · Internal representation
Contextual RNA embeddings
4 · Architecture
RNA Transformer
5 · Learning objective
Masked language modelling
6 · Output representation
Dense vectorsStructure labels / scores

Biological scale

rnanucleotide

Modalities & tasks

RNARepresentationPrediction

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

0 normalized claims

No task, dataset, split and metric claim has been normalized for this record yet.

Open claim intelligence →

Inputs and outputs

Inputs

RNA sequence

Outputs

RNA embeddingsStructure/function predictions

Scientific and technical profile

Scientific principles

RNA language modellingTransfer learning

Technology

Encoder-only Transformer
Ideas before algorithms

Scientific lineage

Explore all foundations

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.

Computational intelligence

Transformer self-attention

Ashish Vaswani and colleagues

Protein, genome, molecule and single-cell foundation models use attention to learn dependencies across biological sequences and multimodal inputs.

Matched concepts: transformer, language model, sequence
Computational intelligence

Information, entropy and communication

Claude E. Shannon

Sequence modelling, cross-entropy training, language models, mutual information and representation learning all use Shannon’s framework.

Matched concepts: language model, sequence, representation

Evaluation evidence

Dataset or evaluationNot yet curated
Task or metricNot yet extracted
Evidence statusNo task-specific benchmark record curated
Open source ↗

BioAtlas has not yet extracted a structured benchmark claim for this record.

Known limitations

  • Performance depends on the evaluation dataset and operating conditions.
  • A structured benchmark claim has not yet been extracted for this record.
  • Outputs require task-specific scientific and experimental validation.

Milestones

Not normalized

Reviewed as a major RNA foundation-model family in 2026 surveys.