rewire.it
benchmark · task

mRNA-protein interaction prediction

This paper-specific evaluation tests mRNA-protein interaction prediction using mRNA-RBP pairs.

1 evaluations · 1 metric rows

At a glance

Explanatory profile: limited source coverage · Automated source review, 2026-09-16. This does not change the review status of its results.

Data, procedure and scoring
PropertyDescription and evidence
Record typePaper-specific task; protocol incompletely extractedGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Table 2, RBP-aware test set row, auROC (%) column
InputsmRNA-RBP pairsGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Table 2, RBP-aware test set row, auROC (%) column
AssessmentAUROCGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Table 2, RBP-aware test set row, auROC (%) column
Recorded split or evaluation settingRBP-aware test setGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Table 2, RBP-aware test set row, auROC (%) column
DatasetsNot extracted or verified for this record.
OrganismsNot extracted or verified for this record.
AssaysNot extracted or verified for this record.
AdaptationNot extracted or verified for this record.
BaselinesNot extracted or verified for this record.

How it works

Reported evaluation outline

Outline of the existing paper extraction. Split membership, fitting details and scorer implementation remain incompletely reviewed.

Reported evaluation outlinemRNA-RBP pairs. Then: Recorded fitting or scoring procedure. Then: Assess AUROCmRNA-RBP pairsRecorded fitting or scoringprocedureAssess AUROC
Read the diagram as text
  1. mRNA-RBP pairs
  2. Recorded fitting or scoring procedure
  3. Assess AUROC
Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Table 2, RBP-aware test set row, auROC (%) column

Evaluation context

The existing paper extraction describes: LLM encoding of protein partner; RBP-aware partition tests generalization to unseen protein diversity. This description is retained with the exact evaluation records; it is not a new protocol reconstruction.

Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Table 2, RBP-aware test set row, auROC (%) column

Tested models and results

Release 2026-09-16-d74d282221a9 · 1 evaluation · 1 metric row. Different protocols are not a single leaderboard.

Results grouped by the exact reported evaluation
Metric and findingCoverage and uncertaintyEvidence
ProteinBERT LLM-encoding model: mRNA-protein interaction prediction

LLM encoding of protein partner; RBP-aware partition tests generalization to unseen protein diversity

Author-reported evaluation · Evaluation metadata: needs review

71.5% AUROC

Unit: percent · Direction: unknown

Uncertainty: not reported in legacy extract

Scored: Not reported · Eligible: Not reported

source checkedGeneralizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity · Table 2, RBP-aware test set row, auROC (%) column

Source checking is not independent reproduction.

Strengths and limitations

Profile review details

Catalogue extraction inspected; protocol claims remain limited to the cited evidence. Missing details are not presumed absent from the original paper.

Stable record: reported-task-d7e6274011946e

Sources and history

Release 2026-09-16-d74d282221a9 · Record review: needs review

Download this release
Technical metadata and extraction receipts

Stable ID: reported-task-d7e6274011946e

areas
rna-transcriptomes
tasks
mRNA-protein interaction prediction
entity level
task
version
Not reported
task
mRNA-protein interaction prediction
scope note
Paper-specific evaluation task; protocol completeness requires further extraction.
missing metadata
protocol version: not_reported_in_legacy_extract
Related records

Suggest a correction