rewire.it
Task

Mean ribosome load from MPRA

Mean ribosome-load prediction includes a dedicated check of compositional generalization beyond the default split.

SourcesmRNABench: A curated benchmark for mature mRNA property and function prediction · Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions

2 evaluations · 2 metric rows

At a glance

Inputs, training, access and other details

Explanatory profile: source reviewed · Automated source review, 2026-09-16. Review applies to the cited claims; unresolved fields are listed below. Numerical results retain their own review status.

Data, procedure and scoring
PropertyDescription and evidence
DatasetsMRL-MPRA sequence/measurement dataset within mRNABench.
SourcesmRNABench: A curated benchmark for mature mRNA property and function prediction · Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions
SplitsThe standard MRL-MPRA evaluation uses a naive random split. A separate compositional-generalization experiment holds out combinations of upstream AUG and Kozak features; neither should be described as the homology split used for other mRNABench tasks.
SourcesmRNABench: A curated benchmark for mature mRNA property and function prediction · Methods: Data Splitting Strategies; Appendix: Compositional Generalization
MetricsPearson correlation and the change between split conditions.
SourcesmRNABench: A curated benchmark for mature mRNA property and function prediction · Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions
BaselinesNaive sequence-feature baseline, randomly initialized Naive Mamba, supervised CNN and multiple frozen foundation-model representations.
SourcesmRNABench: A curated benchmark for mature mRNA property and function prediction · Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions
Leakage controlsThe standard MRL-MPRA split is random. A separate compositional test holds out selected combinations of upstream AUG and Kozak features. Homology-based splits described for other benchmark tasks are not evidence of a homology-disjoint MRL-MPRA split.
SourcesmRNABench: A curated benchmark for mature mRNA property and function prediction · Methods: Data Splitting Strategies; Appendix: Compositional Generalization
UncertaintyThe default split results are averaged over ten random splits with 95% confidence intervals; the compositional split is reported separately.
SourcesmRNABench: A curated benchmark for mature mRNA property and function prediction · Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions
Entity typePaper-specific computational evaluation protocol.
SourcesmRNABench: A curated benchmark for mature mRNA property and function prediction · Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions
OrganismsThe MRL-MPRA task uses synthetic or designed 5′ UTR reporter libraries assayed in human cells. The reporter’s experimental host is distinct from a natural source organism for each synthetic UTR.
SourcesmRNABench: A curated benchmark for mature mRNA property and function prediction · Benchmarking Tasks: Local Tasks, MRL-MPRA
AssaysMassively parallel reporter measurements of mean ribosome load.
SourcesmRNABench: A curated benchmark for mature mRNA property and function prediction · Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions
Allowed inputsmRNA/UTR sequence representations.
SourcesmRNABench: A curated benchmark for mature mRNA property and function prediction · Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions
AdaptationEmbedding-based supervised prediction under default and compositional split conditions.
SourcesmRNABench: A curated benchmark for mature mRNA property and function prediction · Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions

How it works

How it worksComputational evaluation flow
Computational evaluation flow1. Input: mRNA/UTR sequence representations.. Then: 2. Evaluation: The standard MRL-MPRA evaluation uses a naive random split. A separate compositional-generalization experiment holds out combinations of upstream AUG and Kozak features; neither should be described as the homology split used for other mRNABench tasks.. Then: 3. Readout: Pearson correlation and the change between split conditions.Computational evaluation flow1. Input: mRNA/UTR sequence representations.. Then: 2. Evaluation: The standard MRL-MPRA evaluation uses a naive random split. A separate compositional-generalization experiment holds out combinations of upstream AUG and Kozak features; neither should be described as the homology split used for other mRNABench tasks.. Then: 3. Readout: Pearson correlation and the change between split conditions.Computational evaluation flow1. Input: mRNA/UTR sequence representations.. Then: 2. Evaluation: The standard MRL-MPRA evaluation uses a naive random split. A separate compositional-generalization experiment holds out combinations of upstream AUG and Kozak features; neither should be described as the homology split used for other mRNABench tasks.. Then: 3. Readout: Pearson correlation and the change between split conditions.

Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol.

SourcesmRNABench: A curated benchmark for mature mRNA property and function prediction · Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions; Methods: Data Splitting Strategies; Appendix: Compositional Generalization
Evaluation methodology

MRL-MPRA sequence/measurement dataset within mRNABench. The standard MRL-MPRA evaluation uses a naive random split. A separate compositional-generalization experiment holds out combinations of upstream AUG and Kozak features; neither should be described as the homology split used for other mRNABench tasks. Pearson correlation and the change between split conditions. Naive sequence-feature baseline, randomly initialized Naive Mamba, supervised CNN and multiple frozen foundation-model representations. The standard MRL-MPRA split is random. A separate compositional test holds out selected combinations of upstream AUG and Kozak features. Homology-based splits described for other benchmark tasks are not evidence of a homology-disjoint MRL-MPRA split.

SourcesmRNABench: A curated benchmark for mature mRNA property and function prediction · Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions; Methods: Data Splitting Strategies; Appendix: Compositional Generalization

Recorded evaluations

Each evaluation records what was tested and under which conditions.

Tested entities and results

Release 2026-09-17-d277315f7d76 · 2 evaluations · 2 metric rows. Different protocols are not a single leaderboard.

Results grouped by the exact reported evaluation
Metric and findingCoverage and uncertaintyEvidence
RiNALMo: Mean ribosome load from MPRA

Linear probe; mean across ten random seeds.

Independent external evaluation · Evaluation metadata: needs review

0.74 Pearson R

Unit: unitless · Direction: unknown

Uncertainty: not reported in legacy extract

Scored: Not reported · Eligible: Not reported

source checkedmRNABench: A curated benchmark for mature mRNA property and function prediction · Table 2, RiNALMo row, MRL MPRA column

Source checking is not independent reproduction.

RNA-FM: Mean ribosome load from MPRA

Linear probe; mean across ten random seeds.

Independent external evaluation · Evaluation metadata: needs review

0.49 Pearson R

Unit: unitless · Direction: unknown

Uncertainty: not reported in legacy extract

Scored: Not reported · Eligible: Not reported

source checkedmRNABench: A curated benchmark for mature mRNA property and function prediction · Table 2, RNA-FM row, MRL MPRA column

Source checking is not independent reproduction.

Papers and result coverage

Last literature check: 2026-09-17. Primary-paper discovery and source inspection. Source-checked results are not independently reproduced experiments.

Paper or primary resourceVersionReference
mRNABench: A curated benchmark for mature mRNA property and function predictionpreprint archived 2025-07-08Read source
DOI: 10.1101/2025.07.05.662870

What is still missing

  • Table 5 labels localization columns Pearson R while Table 2 labels them AUPRC; quarantine those metric identities pending reconciliation.
  • Appendix C claims ten splits but enumerates nine seeds. Record ten as reported, with discrepancy, not an inferred tenth seed.
  • Tables 5–6 uncertainties are 95% confidence intervals, not standard deviations.
  • Task/subtask pooling and transformed aggregate rankings must not be conflated with printed raw metrics.
Search and extraction details

primary comparison table screened

Searches

  • mRNABench PMC12265608

Evidence locations

  • Tables 1–2, 5–6
  • Appendix B: Mean Ribosome Load – MPRA
  • Appendix C: Linear Probing Experimental Setup
  • Appendix D

Strengths and limitations

Strengths supported by sources

No source-reviewed explanatory claims are recorded here yet.

Limitations and conditions

  • Compositional testing changes the feature combinations represented in training and testing.
    SourcesmRNABench: A curated benchmark for mature mRNA property and function prediction · Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions
Profile review details

Targeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change.

Stable record: reported-task-57dc3dcdb67a81

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.

17 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-09-17-d277315f7d76
Property and statementOriginal source and locationReview and provenance
Diagram caption

Conceptual summary of the cited evaluation; exact task configuration and source version remain part of the protocol.

Individual claims
mRNABench: A curated benchmark for mature mRNA property and function prediction

Original source ↗

Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions; Methods: Data Splitting Strategies; Appendix: Compositional Generalization

Version: preprint archived 2025-07-08
Retrieved: 2026-09-16T10:41:16.497221+00:00

source checked

automated source review · 2026-09-16

Audit details

Targeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change.

Field: attributes.profile.diagram.caption

Source artifact SHA-256: 79f6264ee883535203c63a313547e7c57baa85585f76b42f8d899eb17fb7e600

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Diagram steps

["Input: mRNA/UTR sequence representations.","Evaluation: The standard MRL-MPRA evaluation uses a naive random split. A separate compositional-generalization experiment holds out combinations of upstream AUG and Kozak features; neither should be described as the homology split used for other mRNABench tasks.","Readout: Pearson correlation and the change between split conditions."]

Individual claims
mRNABench: A curated benchmark for mature mRNA property and function prediction

Original source ↗

Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions; Methods: Data Splitting Strategies; Appendix: Compositional Generalization

Version: preprint archived 2025-07-08
Retrieved: 2026-09-16T10:41:16.497221+00:00

source checked

automated source review · 2026-09-16

Audit details

Targeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change.

Field: attributes.profile.diagram.steps

Source artifact SHA-256: 79f6264ee883535203c63a313547e7c57baa85585f76b42f8d899eb17fb7e600

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Diagram title

Computational evaluation flow

Individual claims
mRNABench: A curated benchmark for mature mRNA property and function prediction

Original source ↗

Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions; Methods: Data Splitting Strategies; Appendix: Compositional Generalization

Version: preprint archived 2025-07-08
Retrieved: 2026-09-16T10:41:16.497221+00:00

source checked

automated source review · 2026-09-16

Audit details

Targeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change.

Field: attributes.profile.diagram.title

Source artifact SHA-256: 79f6264ee883535203c63a313547e7c57baa85585f76b42f8d899eb17fb7e600

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Datasets

MRL-MPRA sequence/measurement dataset within mRNABench.

Individual claims
mRNABench: A curated benchmark for mature mRNA property and function prediction

Original source ↗

Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions

Version: preprint archived 2025-07-08
Retrieved: 2026-09-16T10:41:16.497221+00:00

source checked

automated source review · 2026-09-16

Audit details

Targeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change.

Field: attributes.profile.facts.0.value

Source artifact SHA-256: 79f6264ee883535203c63a313547e7c57baa85585f76b42f8d899eb17fb7e600

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Splits

The standard MRL-MPRA evaluation uses a naive random split. A separate compositional-generalization experiment holds out combinations of upstream AUG and Kozak features; neither should be described as the homology split used for other mRNABench tasks.

Individual claims
mRNABench: A curated benchmark for mature mRNA property and function prediction

Original source ↗

Methods: Data Splitting Strategies; Appendix: Compositional Generalization

Version: preprint archived 2025-07-08
Retrieved: 2026-09-16T10:41:16.497221+00:00

source checked

automated source review · 2026-09-16

Audit details

Targeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change.

Field: attributes.profile.facts.1.value

Source artifact SHA-256: 79f6264ee883535203c63a313547e7c57baa85585f76b42f8d899eb17fb7e600

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Adaptation

Embedding-based supervised prediction under default and compositional split conditions.

Individual claims
mRNABench: A curated benchmark for mature mRNA property and function prediction

Original source ↗

Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions

Version: preprint archived 2025-07-08
Retrieved: 2026-09-16T10:41:16.497221+00:00

source checked

automated source review · 2026-09-16

Audit details

Targeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change.

Field: attributes.profile.facts.10.value

Source artifact SHA-256: 79f6264ee883535203c63a313547e7c57baa85585f76b42f8d899eb17fb7e600

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Metrics

Pearson correlation and the change between split conditions.

Individual claims
mRNABench: A curated benchmark for mature mRNA property and function prediction

Original source ↗

Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions

Version: preprint archived 2025-07-08
Retrieved: 2026-09-16T10:41:16.497221+00:00

source checked

automated source review · 2026-09-16

Audit details

Targeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change.

Field: attributes.profile.facts.2.value

Source artifact SHA-256: 79f6264ee883535203c63a313547e7c57baa85585f76b42f8d899eb17fb7e600

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Baselines

Naive sequence-feature baseline, randomly initialized Naive Mamba, supervised CNN and multiple frozen foundation-model representations.

Individual claims
mRNABench: A curated benchmark for mature mRNA property and function prediction

Original source ↗

Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions

Version: preprint archived 2025-07-08
Retrieved: 2026-09-16T10:41:16.497221+00:00

source checked

automated source review · 2026-09-16

Audit details

Targeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change.

Field: attributes.profile.facts.3.value

Source artifact SHA-256: 79f6264ee883535203c63a313547e7c57baa85585f76b42f8d899eb17fb7e600

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Leakage controls

The standard MRL-MPRA split is random. A separate compositional test holds out selected combinations of upstream AUG and Kozak features. Homology-based splits described for other benchmark tasks are not evidence of a homology-disjoint MRL-MPRA split.

Individual claims
mRNABench: A curated benchmark for mature mRNA property and function prediction

Original source ↗

Methods: Data Splitting Strategies; Appendix: Compositional Generalization

Version: preprint archived 2025-07-08
Retrieved: 2026-09-16T10:41:16.497221+00:00

source checked

automated source review · 2026-09-16

Audit details

Targeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change.

Field: attributes.profile.facts.4.value

Source artifact SHA-256: 79f6264ee883535203c63a313547e7c57baa85585f76b42f8d899eb17fb7e600

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Uncertainty

The default split results are averaged over ten random splits with 95% confidence intervals; the compositional split is reported separately.

Individual claims
mRNABench: A curated benchmark for mature mRNA property and function prediction

Original source ↗

Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions

Version: preprint archived 2025-07-08
Retrieved: 2026-09-16T10:41:16.497221+00:00

source checked

automated source review · 2026-09-16

Audit details

Targeted full-paper and supplement review of the outstanding task fields, with original dataset metadata checked where accessible. Source-scoped omissions are explicit; no independent benchmark reproduction or numerical-result change.

Field: attributes.profile.facts.5.value

Source artifact SHA-256: 79f6264ee883535203c63a313547e7c57baa85585f76b42f8d899eb17fb7e600

Hash scope: Hash scope not separately documented; inspect source record

Inspected artifact

Sources and history

Release 2026-09-17-d277315f7d76 · Record review: needs review

2 source records and release historyDownload this release
Technical metadata and extraction receipts

Stable ID: reported-task-57dc3dcdb67a81

areas
rna-transcriptomes
tasks
Mean ribosome load from MPRA
entity level
task
version
Not reported
task
Mean ribosome load from MPRA
scope note
Paper-specific evaluation task; protocol completeness requires further extraction.
benchmark research
review date: 2026-09-17; status: primary_comparison_table_screened; primary sources: expansion-p3-mrnabench-2025; inspected locators: Tables 1–2, 5–6; Appendix B: Mean Ribosome Load – MPRA; Appendix C: Linear Probing Experimental Setup; Appendix D; searched queries: mRNABench PMC12265608; gaps: Table 5 labels localization columns Pearson R while Table 2 labels them AUPRC; quarantine those metric identities pending reconciliation.; Appendix C claims ten splits but enumerates nine seeds. Record ten as reported, with discrepancy, not an inferred tenth seed.; Tables 5–6 uncertainties are 95% confidence intervals, not standard deviations.; Task/subtask pooling and transformed aggregate rankings must not be conflated with printed raw metrics.; claim scope: Primary-paper discovery and source inspection. Source-checked results are not independently reproduced experiments.
historical missing metadata
protocol version: not_reported_in_legacy_extract; split: not_reported_in_legacy_extract
metadata review scope
historical_missing_metadata preserves the original discovery state. Current descriptive evidence and missingness are recorded in profile.facts; numerical-result review is separate.
legacy kinds
benchmark
entity classification
review date: 2026-09-17; rationale: This source-scoped record identifies the biological prediction task and holds its paper context. Preserve the existing task identity; exact split, model adaptation and scoring remain in linked evaluations or separate protocol records.; source ids: mrnabench-2025; source locator: Benchmarking Tasks; compositional generalization analysis; cached text lines 24, 26–27, 190–193; default-split results table caption and compositional-split results; matching task comparison table/ablation captions; ambiguities: A paper- or suite-specific task may constrain some inputs or metrics; that alone does not make it interchangeable with a complete versioned protocol. No protocol equivalence is inferred.; Some legacy profile Entity type facts use the generic phrase computational evaluation protocol. That boilerplate is not sufficient to establish a single fixed protocol identity or to merge this task with another protocol record.
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