Model type
Masked-token RNA transformer encoder
Configuration
RNA-FM
RNA-FM learns contextual representations of RNA nucleotides for downstream RNA analyses.
Sources (2)
ml4bio/RNA-FM: README.md; ml4bio/RNA-FM: fm/pretrained.py · README.md: Quick Start, embedding examples and RNA foundation model comparison tableHow it worksRNA-FM workflow
Conceptual summary of the documented data flow; optional inputs and configured downstream stages must be reported for a reproducible evaluation.
Sources (2)
ml4bio/RNA-FM: README.md; ml4bio/RNA-FM: fm/pretrained.py · README.md: Quick Start, embedding examples and RNA foundation model comparison tableOverview
Inputs
RNA sequences tokenized at nucleotide resolution.
Outputs
Contextual token embeddings for a specified downstream RNA task.
Access
Official project documentation and implementation: https://github.com/ml4bio/RNA-FM
Sources (2)
ml4bio/RNA-FM: README.md; ml4bio/RNA-FM: fm/pretrained.py · README.md: Quick Start, embedding examples and RNA foundation model comparison tablelimited source coverage · Automated source review, 2026-09-23. All specifications and missing details
Evaluations and results
43 evaluations · 43 results. Different protocols are not a single leaderboard.
Filter evaluations
Applied filters: All linked evaluations
| Tested configuration | Protocol and dataset | Finding | Evidence and details |
|---|---|---|---|
| Configuration: RNA-FM | Task: NABench CCV-CORR-APTAMER: Fitness prediction on aptamer assays, supervised, contiguous cross validation Dataset subset: NABench aptamer assays (NABench split) | 0.115 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceScored supervised, contiguous cross validation across the NABench aptamer assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 11, row(RNA-FM), column(aptamer) |
| Configuration: RNA-FM | Task: NABench CCV-CORR-ENHANCER: Fitness prediction on enhancer assays, supervised, contiguous cross validation Dataset subset: NABench enhancer assays (NABench split) | 0.095 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceScored supervised, contiguous cross validation across the NABench enhancer assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 11, row(RNA-FM), column(enhancer) |
| Configuration: RNA-FM | Task: NABench CCV-CORR-MRNA: Fitness prediction on mRNA assays, supervised, contiguous cross validation Dataset subset: NABench mRNA assays (NABench split) | 0.259 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceScored supervised, contiguous cross validation across the NABench mRNA assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 11, row(RNA-FM), column(mRNA) |
| Configuration: RNA-FM | Task: NABench CCV-CORR-PROMOTER: Fitness prediction on promoter assays, supervised, contiguous cross validation Dataset subset: NABench promoter assays (NABench split) | 0.035 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceScored supervised, contiguous cross validation across the NABench promoter assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 11, row(RNA-FM), column(promoter) |
| Configuration: RNA-FM | Task: NABench CCV-CORR-RIBOZYME: Fitness prediction on ribozyme assays, supervised, contiguous cross validation Dataset subset: NABench ribozyme assays (NABench split) | 0.242 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceScored supervised, contiguous cross validation across the NABench ribozyme assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 11, row(RNA-FM), column(ribozyme) |
| Configuration: RNA-FM | Task: NABench CCV-CORR-TRNA: Fitness prediction on tRNA assays, supervised, contiguous cross validation Dataset subset: NABench tRNA assays (NABench split) | 0.345 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceScored supervised, contiguous cross validation across the NABench tRNA assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 11, row(RNA-FM), column(tRNA) |
| Configuration: RNA-FM | Task: NABench DMS-CCV: Overall fitness prediction on NABench deep mutational scanning assays, Contiguous cross validation Spearman ρ Dataset subset: NABench deep mutational scanning assays (NABench split) | 0.233 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceAggregated by the NABench authors across NABench deep mutational scanning assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 6, row(RNA-FM), column(Contiguous cross validation Spearman ρ) |
| Configuration: RNA-FM | Task: NABench DMS-FS: Overall fitness prediction on NABench deep mutational scanning assays, Few-shot Spearman ρ Dataset subset: NABench deep mutational scanning assays (NABench split) | 0.183 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceAggregated by the NABench authors across NABench deep mutational scanning assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 6, row(RNA-FM), column(Few-shot Spearman ρ) |
| Configuration: RNA-FM | Task: NABench DMS-RCV: Overall fitness prediction on NABench deep mutational scanning assays, Random cross validation Spearman ρ Dataset subset: NABench deep mutational scanning assays (NABench split) | 0.544 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceAggregated by the NABench authors across NABench deep mutational scanning assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 6, row(RNA-FM), column(Random cross validation Spearman ρ) |
| Configuration: RNA-FM | Task: NABench DMS-ZS-AUC: Overall fitness prediction on NABench deep mutational scanning assays, Zero-shot AUC Dataset subset: NABench deep mutational scanning assays (NABench split) | 0.541 auc fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceAggregated by the NABench authors across NABench deep mutational scanning assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 6, row(RNA-FM), column(Zero-shot AUC) |
| Configuration: RNA-FM | Task: NABench DMS-ZS-CORR: Overall fitness prediction on NABench deep mutational scanning assays, Zero-shot Spearman ρ Dataset subset: NABench deep mutational scanning assays (NABench split) | 0.148 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceAggregated by the NABench authors across NABench deep mutational scanning assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 6, row(RNA-FM), column(Zero-shot Spearman ρ) |
| Configuration: RNA-FM | Task: NABench DMS-ZS-MCC: Overall fitness prediction on NABench deep mutational scanning assays, Zero-shot MCC Dataset subset: NABench deep mutational scanning assays (NABench split) | 0.068 mcc correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceAggregated by the NABench authors across NABench deep mutational scanning assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 6, row(RNA-FM), column(Zero-shot MCC) |
| Configuration: RNA-FM | Task: NABench DMS-ZS-NDCG: Overall fitness prediction on NABench deep mutational scanning assays, Zero-shot NDCG Dataset subset: NABench deep mutational scanning assays (NABench split) | 0.38 ndcg fraction · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceAggregated by the NABench authors across NABench deep mutational scanning assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 6, row(RNA-FM), column(Zero-shot NDCG) |
| Configuration: RNA-FM | Task: NABench FS-CORR-APTAMER: Fitness prediction on aptamer assays, few-shot Dataset subset: NABench aptamer assays (NABench split) | 0.248 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceRNA-FM on NABench FS-CORR-APTAMER: Fitness prediction on aptamer assays, few-shot Scored few-shot across the NABench aptamer assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 9, row(RNA-FM), column(aptamer) |
| Configuration: RNA-FM | Task: NABench FS-CORR-ENHANCER: Fitness prediction on enhancer assays, few-shot Dataset subset: NABench enhancer assays (NABench split) | 0.009 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceRNA-FM on NABench FS-CORR-ENHANCER: Fitness prediction on enhancer assays, few-shot Scored few-shot across the NABench enhancer assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 9, row(RNA-FM), column(enhancer) |
| Configuration: RNA-FM | Task: NABench FS-CORR-MRNA: Fitness prediction on mRNA assays, few-shot Dataset subset: NABench mRNA assays (NABench split) | 0.318 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceRNA-FM on NABench FS-CORR-MRNA: Fitness prediction on mRNA assays, few-shot Scored few-shot across the NABench mRNA assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 9, row(RNA-FM), column(mRNA) |
| Configuration: RNA-FM | Task: NABench FS-CORR-PROMOTER: Fitness prediction on promoter assays, few-shot Dataset subset: NABench promoter assays (NABench split) | 0.159 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceRNA-FM on NABench FS-CORR-PROMOTER: Fitness prediction on promoter assays, few-shot Scored few-shot across the NABench promoter assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 9, row(RNA-FM), column(promoter) |
| Configuration: RNA-FM | Task: NABench FS-CORR-RIBOZYME: Fitness prediction on ribozyme assays, few-shot Dataset subset: NABench ribozyme assays (NABench split) | 0.164 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceRNA-FM on NABench FS-CORR-RIBOZYME: Fitness prediction on ribozyme assays, few-shot Scored few-shot across the NABench ribozyme assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 9, row(RNA-FM), column(ribozyme) |
| Configuration: RNA-FM | Task: NABench FS-CORR-TRNA: Fitness prediction on tRNA assays, few-shot Dataset subset: NABench tRNA assays (NABench split) | 0.232 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceRNA-FM on NABench FS-CORR-TRNA: Fitness prediction on tRNA assays, few-shot Scored few-shot across the NABench tRNA assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 9, row(RNA-FM), column(tRNA) |
| Configuration: RNA-FM | Task: NABench RCV-CORR-APTAMER: Fitness prediction on aptamer assays, supervised, random cross validation Dataset subset: NABench aptamer assays (NABench split) | 0.53 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceScored supervised, random cross validation across the NABench aptamer assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 10, row(RNA-FM), column(aptamer) |
| Configuration: RNA-FM | Task: NABench RCV-CORR-ENHANCER: Fitness prediction on enhancer assays, supervised, random cross validation Dataset subset: NABench enhancer assays (NABench split) | 0.256 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceScored supervised, random cross validation across the NABench enhancer assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 10, row(RNA-FM), column(enhancer) |
| Configuration: RNA-FM | Task: NABench RCV-CORR-MRNA: Fitness prediction on mRNA assays, supervised, random cross validation Dataset subset: NABench mRNA assays (NABench split) | 0.597 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceScored supervised, random cross validation across the NABench mRNA assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 10, row(RNA-FM), column(mRNA) |
| Configuration: RNA-FM | Task: NABench RCV-CORR-PROMOTER: Fitness prediction on promoter assays, supervised, random cross validation Dataset subset: NABench promoter assays (NABench split) | 0.582 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceScored supervised, random cross validation across the NABench promoter assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 10, row(RNA-FM), column(promoter) |
| Configuration: RNA-FM | Task: NABench RCV-CORR-RIBOZYME: Fitness prediction on ribozyme assays, supervised, random cross validation Dataset subset: NABench ribozyme assays (NABench split) | 0.556 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceScored supervised, random cross validation across the NABench ribozyme assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 10, row(RNA-FM), column(ribozyme) |
| Configuration: RNA-FM | Task: NABench RCV-CORR-TRNA: Fitness prediction on tRNA assays, supervised, random cross validation Dataset subset: NABench tRNA assays (NABench split) | 0.587 spearman correlation · higher Uncertainty: Not reported Coverage: Not reported scored / Not reported eligible | Author-reported evaluation · Source checkedMethods, coverage and sourceScored supervised, random cross validation across the NABench tRNA assays. Aggregation: Not reported NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Table 10, row(RNA-FM), column(tRNA) |
Source checking is not independent reproduction. Release 2026-09-29-06401fd5b220.
Use this model
How it works, versions and access
Related profile: RNA-FM. This page retains the exact record and its evaluation context.
This configuration
Nucleotide foundation model evaluated by the NABench authors under their fitness prediction protocol.
- record
- RNA-FM
- configuration
- Not reported
- entity type
- Configuration
How it works
How it works
RNA-FM converts an RNA sequence into one token per nucleotide. Twelve transformer encoder blocks use self-attention to produce a 640-dimensional representation at each position. During pretraining, the model learns to recover masked nucleotides from their surrounding sequence. The resulting representations can be supplied to a separately specified downstream model; they are not, by themselves, a structure or functional prediction.
Sources
RNA-FM original methods, arXiv v5 · arXiv:2204.00300v5, Methods: ncRNA data collection and preprocessing and RNA foundation model training details (p.22)Versions and reproducibility
The nucleotide-based rna_fm_t12 and codon-based mrna_fm_t12 interfaces are distinct. The original RNA-FM paper sets a training input-length limit of 1,024 and describes a usable limit of 1,022 nucleotides. That limit should not be assigned to mRNA-FM without checking its separate configuration.
Sources (3)
ml4bio/RNA-FM: README.md; ml4bio/RNA-FM: fm/pretrained.py; RNA-FM original methods, arXiv v5 · Official README: Quick Start and RNA-FM/mRNA-FM examples; arXiv:2204.00300v5, Methods: training input length and SARS-CoV-2 genome embedding extraction (pp.22–23)Strengths, limitations and unresolved questions
Strengths and limitations
Strengths and considerations
- The repository exposes embedding extraction and examples for downstream RNA analyses.
Sources (2)
ml4bio/RNA-FM: README.md; ml4bio/RNA-FM: fm/pretrained.py · README.md: Quick Start, embedding examples and RNA foundation model comparison table
Limitations and conditions
- Base-level RNA-FM and codon-level mRNA-FM are not interchangeable. The task head and tokenization must be specified in each evaluation.
Sources (2)
ml4bio/RNA-FM: README.md; ml4bio/RNA-FM: fm/pretrained.py · README.md: Quick Start, embedding examples and RNA foundation model comparison table
Profile review details
Follow-up review of Checkpoint identification, Training cutoff, Weights licence. Source locations and before/after decisions are recorded in the 23 September profile-evidence audit. Other explanatory content retains its earlier source scope. No human scientific review or independent reproduction is implied.
Stable record: discovery-model-rna-fmSpecifications
Inputs, training, access and other details
Explanatory profile: limited source coverage · Automated source review, 2026-09-23. Review applies to the cited claims; unresolved fields are listed below. Numerical results retain their own review status.
| Property | Description and evidence |
|---|---|
| Model type | Masked-token RNA transformer encoderSources (2)ml4bio/RNA-FM: README.md; ml4bio/RNA-FM: fm/pretrained.py · README.md: Quick Start, embedding examples and RNA foundation model comparison table |
| Architecture | 12-layer masked-token transformer encoder with hidden width 640 and 20 attention heads; nucleotide tokens produce contextual representations.SourcesRNA-FM original methods, arXiv v5 · arXiv:2204.00300v5, Methods: ncRNA data collection and preprocessing and RNA foundation model training details (p.22) |
| Inputs | RNA sequences tokenized at nucleotide resolution.Sources (2)ml4bio/RNA-FM: README.md; ml4bio/RNA-FM: fm/pretrained.py · README.md: Quick Start, embedding examples and RNA foundation model comparison table |
| Outputs | Contextual token embeddings for a specified downstream RNA task.Sources (2)ml4bio/RNA-FM: README.md; ml4bio/RNA-FM: fm/pretrained.py · README.md: Quick Start, embedding examples and RNA foundation model comparison table |
| Parameters | 99M, as printed in the official Foundation Models table.Sources (2)ml4bio/RNA-FM: README.md; ml4bio/RNA-FM: fm/pretrained.py · README.md: Quick Start, embedding examples and RNA foundation model comparison table |
| Known versions | rna_fm_t12 and mrna_fm_t12 are separate pretrained interfaces.Sources (2)ml4bio/RNA-FM: README.md; ml4bio/RNA-FM: fm/pretrained.py · README.md: Quick Start, embedding examples and RNA foundation model comparison table |
| Training data | 23.7 million non-coding RNA sequences collected from RNAcentral. The authors replace T with U and remove identical sequences using CD-HIT-EST at 100% identity, naming the resulting corpus RNAcentral100.SourcesRNA-FM original methods, arXiv v5 · arXiv:2204.00300v5, Methods: ncRNA data collection and preprocessing and RNA foundation model training details (p.22) |
| Training cutoff | The original Methods describe RNAcentral100 preprocessing but do not establish a dated RNAcentral release. RNAcentral100 is a processed-corpus label, not a release number. · Not reported in inspected sourcesSourcesRNA-FM original methods, arXiv v5 · arXiv2204.00300v5, Methods: Large-scale pre-training dataset, p22 |
| Context limits | The original paper sets a training input-length limit of 1,024 and describes a usable input limit of 1,022 nucleotides. These are the original RNA-FM settings, not a validated limit for later codon-based mRNA-FM checkpoints.SourcesRNA-FM original methods, arXiv v5 · arXiv:2204.00300v5, Methods: RNA foundation model training details (pp.22–23) and RNA-FM application input-limit statement (p.23) |
| Weights licence | The official model-comparison table labels RNA-FM MIT, while the README footer explicitly refers to source code. Separate checkpoint-distribution terms remain unverified; the linked weight repository could not be retrieved during this review. · Not reported in inspected sourcesSources (3)ml4bio/RNA-FM: README.md; ml4bio/RNA-FM: LICENSE; rnafm loader: primary artifact · README: Related RNA Language Models, RNA-FM row; License footer; LICENSE. Linked https://huggingface.co/ml4bio/RNA-FM returned HTTP401 during the review. |
| Access | Official project documentation and implementation: https://github.com/ml4bio/RNA-FMSources (2)ml4bio/RNA-FM: README.md; ml4bio/RNA-FM: fm/pretrained.py · README.md: Quick Start, embedding examples and RNA foundation model comparison table |
| Code licence | MITSourcesml4bio/RNA-FM: LICENSE · LICENSE: licence text |
| Checkpoint identification | The official rna_fm_t12 loader downloads RNA-FM_pretrained.pth from the authors’ server. The inspected loader does not pin a checkpoint revision or publish a digest; a code commit alone cannot identify the bytes used by a published evaluation. · Not reported in inspected sourcesSourcesrnafm loader: primary artifact · load_fm_model_and_alphabet_hub: rna_fm_t12 branch, lines155–162 |
Evidence
Source checking verifies the cited claim or transcription. It does not establish independent reproduction.
Evidence table
Inspect claims, sources and review details
Trace each statement to its source and review. A context-only reference supports the record generally; it does not verify an individual field. Source checking does not reproduce an experiment.
One row per statement and cited source. Multiple citations are not independent evaluations. Shared locators are labelled explicitly.
2 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Relationship: family discovery-model-rna-fm Individual claims | NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction BEACON Table 2 and Appendix A.3.2 RNAcentral ncRNA model, Table 3; mRNABench Table 2 and Appendix inventory; NABench model inventory; source-labelled configuration RNA-FM | Existing reviewed locator: Table 7, row(RNA-FM) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256 | source checked automated source review · 2026-09-23 Audit detailsSource review establishes this relationship only. Exact evaluated configurations and original numerical review status remain unchanged. RNA-FM ncRNA backbone explicitly identified; mRNA-FM is distinct and must not inherit these scores. Field: Claim: model-evaluation-identity-c9423272036bfaade5f5 Source artifact SHA-256: Hash scope: Exact retrieved primary paper artifact bytes. |
| Relationship: family discovery-model-rna-fm Individual claims | ml4bio/RNA-FM: README.md BEACON Table 2 and Appendix A.3.2 RNAcentral ncRNA model, Table 3; mRNABench Table 2 and Appendix inventory; NABench model inventory; source-labelled configuration RNA-FM | Existing reviewed locator: Table 7, row(RNA-FM) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: 348951516e0963d22bbb33b3c9fc18c89081d38e | source checked automated source review · 2026-09-23 Audit detailsSource review establishes this relationship only. Exact evaluated configurations and original numerical review status remain unchanged. RNA-FM ncRNA backbone explicitly identified; mRNA-FM is distinct and must not inherit these scores. Field: Claim: model-evaluation-identity-c9423272036bfaade5f5 Source artifact SHA-256: Hash scope: SHA-256 of retrieved original artifact bytes Format: original_artifact |
Sources and history
View linked audit checks and correction history
Release 2026-09-29-06401fd5b220 · Record review: source checked
1 source records and release history
- NABench: Large-Scale Benchmarks of Nucleotide Foundation Models for Fitness Prediction · Original source · Primary full-text snapshot retrieved 2026-09-17; exact bytes pinned by SHA-256
Technical metadata and extraction receipts
Stable ID: nabench-method-rna-fm
- areas
- rna-transcriptomes
- source locator
- Table 7, row(RNA-FM)
- missing metadata
- checkpoint revision: unreported; parameters: unextracted
Related records
- family: RNA-FM
- subject: RNA-FM: family RNA-FM
- model: RNA-FM on NABench CCV-CORR-APTAMER: Fitness prediction on aptamer assays, supervised, contiguous cross validation
- model: RNA-FM on NABench CCV-CORR-ENHANCER: Fitness prediction on enhancer assays, supervised, contiguous cross validation
- model: RNA-FM on NABench CCV-CORR-MRNA: Fitness prediction on mRNA assays, supervised, contiguous cross validation
- model: RNA-FM on NABench CCV-CORR-PROMOTER: Fitness prediction on promoter assays, supervised, contiguous cross validation
- model: RNA-FM on NABench CCV-CORR-RIBOZYME: Fitness prediction on ribozyme assays, supervised, contiguous cross validation
- model: RNA-FM on NABench CCV-CORR-TRNA: Fitness prediction on tRNA assays, supervised, contiguous cross validation
- model: RNA-FM on NABench DMS-CCV: Overall fitness prediction on NABench deep mutational scanning assays, Contiguous cross validation Spearman ρ
- model: RNA-FM on NABench DMS-FS: Overall fitness prediction on NABench deep mutational scanning assays, Few-shot Spearman ρ
- model: RNA-FM on NABench DMS-RCV: Overall fitness prediction on NABench deep mutational scanning assays, Random cross validation Spearman ρ
- model: RNA-FM on NABench DMS-ZS-AUC: Overall fitness prediction on NABench deep mutational scanning assays, Zero-shot AUC
- model: RNA-FM on NABench DMS-ZS-CORR: Overall fitness prediction on NABench deep mutational scanning assays, Zero-shot Spearman ρ
- model: RNA-FM on NABench DMS-ZS-MCC: Overall fitness prediction on NABench deep mutational scanning assays, Zero-shot MCC
- model: RNA-FM on NABench DMS-ZS-NDCG: Overall fitness prediction on NABench deep mutational scanning assays, Zero-shot NDCG
- model: RNA-FM on NABench FS-CORR-APTAMER: Fitness prediction on aptamer assays, few-shot
- model: RNA-FM on NABench FS-CORR-ENHANCER: Fitness prediction on enhancer assays, few-shot
- model: RNA-FM on NABench FS-CORR-MRNA: Fitness prediction on mRNA assays, few-shot
- model: RNA-FM on NABench FS-CORR-PROMOTER: Fitness prediction on promoter assays, few-shot
- model: RNA-FM on NABench FS-CORR-RIBOZYME: Fitness prediction on ribozyme assays, few-shot
- model: RNA-FM on NABench FS-CORR-TRNA: Fitness prediction on tRNA assays, few-shot
- model: RNA-FM on NABench RCV-CORR-APTAMER: Fitness prediction on aptamer assays, supervised, random cross validation
- model: RNA-FM on NABench RCV-CORR-ENHANCER: Fitness prediction on enhancer assays, supervised, random cross validation
- model: RNA-FM on NABench RCV-CORR-MRNA: Fitness prediction on mRNA assays, supervised, random cross validation
- model: RNA-FM on NABench RCV-CORR-PROMOTER: Fitness prediction on promoter assays, supervised, random cross validation
- model: RNA-FM on NABench RCV-CORR-RIBOZYME: Fitness prediction on ribozyme assays, supervised, random cross validation
- model: RNA-FM on NABench RCV-CORR-TRNA: Fitness prediction on tRNA assays, supervised, random cross validation
- model: RNA-FM on NABench SELEX-FS: Overall fitness prediction on NABench SELEX assays, Few-shot Spearman ρ
- model: RNA-FM on NABench SELEX-RCV: Overall fitness prediction on NABench SELEX assays, Random cross validation Spearman ρ
- model: RNA-FM on NABench SELEX-ZS-AUC: Overall fitness prediction on NABench SELEX assays, Zero-shot AUC
- model: RNA-FM on NABench SELEX-ZS-CORR: Overall fitness prediction on NABench SELEX assays, Zero-shot Spearman ρ
- model: RNA-FM on NABench SELEX-ZS-MCC: Overall fitness prediction on NABench SELEX assays, Zero-shot MCC
- model: RNA-FM on NABench SELEX-ZS-NDCG: Overall fitness prediction on NABench SELEX assays, Zero-shot NDCG
- model: RNA-FM on NABench ZS-AUC-APTAMER: Fitness prediction on aptamer assays, zero-shot
- model: RNA-FM on NABench ZS-AUC-ENHANCER: Fitness prediction on enhancer assays, zero-shot
- model: RNA-FM on NABench ZS-AUC-MRNA: Fitness prediction on mRNA assays, zero-shot
- model: RNA-FM on NABench ZS-AUC-PROMOTER: Fitness prediction on promoter assays, zero-shot
- model: RNA-FM on NABench ZS-AUC-RIBOZYME: Fitness prediction on ribozyme assays, zero-shot
- model: RNA-FM on NABench ZS-AUC-TRNA: Fitness prediction on tRNA assays, zero-shot
- model: RNA-FM on NABench ZS-CORR-APTAMER: Fitness prediction on aptamer assays, zero-shot
- model: RNA-FM on NABench ZS-CORR-ENHANCER: Fitness prediction on enhancer assays, zero-shot
- model: RNA-FM on NABench ZS-CORR-MRNA: Fitness prediction on mRNA assays, zero-shot
- model: RNA-FM on NABench ZS-CORR-PROMOTER: Fitness prediction on promoter assays, zero-shot
- model: RNA-FM on NABench ZS-CORR-RIBOZYME: Fitness prediction on ribozyme assays, zero-shot
- model: RNA-FM on NABench ZS-CORR-TRNA: Fitness prediction on tRNA assays, zero-shot