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

RNA-FM

A foundational BERT-style model for non-coding RNA sequence representations.

2/7Evidence fields documented
60-SECOND EVALUATION VIEW

What should a scientist know before using RNA-FM?

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

RNA-FM learns general-purpose structural and functional information from large RNA sequence corpora and anchors an ecosystem that includes RhoFold and RNA design methods.

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.

Sources2 connectedPrimary resources and normalized claims
Claims0 normalizedNo normalized claim yet
EntityRNA-FMmodel-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
OrganizationML4Bio / academic collaborators
Model family introducedNot normalized
AccessOpen source
Commercial useAllowed / verify checkpoint terms
DeploymentSelf-hosted
ComputeGPU recommended
Domainsrna
Biology → representation → computation → evidence

How RNA-FM 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 vectorsPairing probabilities / 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 embeddingsBase-pairing / downstream predictions

Scientific and technical profile

Scientific principles

RNA language modellingSelf-supervised learning

Technology

BERT-style 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

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, information, sequence
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

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

The project reports pretraining on more than 23 million non-coding RNA sequences.