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benchmark · task

protein localization classification

This paper-specific evaluation tests protein localization classification using HPA-FoV.

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 extractedCell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy · Table 2, HPA-FoV section, Cell-DINO row, PL column
InputsHPA-FoVCell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy · Table 2, HPA-FoV section, Cell-DINO row, PL column
AssessmentF1Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy · Table 2, HPA-FoV section, Cell-DINO row, PL column
Recorded split or evaluation settingUnextractedCell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy · Table 2, HPA-FoV section, Cell-DINO row, PL 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 outlineHPA-FoV. Then: Recorded fitting or scoring procedure. Then: Assess F1HPA-FoVRecorded fitting or scoringprocedureAssess F1
Read the diagram as text
  1. HPA-FoV
  2. Recorded fitting or scoring procedure
  3. Assess F1
Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy · Table 2, HPA-FoV section, Cell-DINO row, PL column

Evaluation context

The existing paper extraction describes: Self-supervised microscopy embedding pre-trained on HPA-FoV; downstream protein-localization classifier. Dataset-specific pretraining; the paper does not claim a general-purpose foundation model that generalizes beyond these benchmarks. This description is retained with the exact evaluation records; it is not a new protocol reconstruction.

Cell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy · Table 2, HPA-FoV section, Cell-DINO row, PL 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
Cell-DINO ViT-L: protein localization classification

Self-supervised microscopy embedding pre-trained on HPA-FoV; downstream protein-localization classifier. Dataset-specific pretraining; the paper does not claim a general-purpose foundation model that generalizes beyond these benchmarks.

Author-reported evaluation · Evaluation metadata: needs review

65.5% F1

Unit: percent · Direction: unknown

Uncertainty: not reported in legacy extract

Scored: Not reported · Eligible: Not reported

source checkedCell-DINO: Self-supervised image-based embeddings for cell fluorescent microscopy · Table 2, HPA-FoV section, Cell-DINO row, PL 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-7621fa1be55362

Sources and history

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

Download this release
Technical metadata and extraction receipts

Stable ID: reported-task-7621fa1be55362

areas
cells-tissues
tasks
protein localization classification
entity level
task
version
Not reported
task
protein localization classification
scope note
Paper-specific evaluation task; protocol completeness requires further extraction.
missing metadata
protocol version: not_reported_in_legacy_extract; split: not_reported_in_legacy_extract
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