rewirebio.iobenchmarks
Evaluation

MLP on summed gene embeddings, trained once on a downsampled set on Horlbeck K562 (Tables 1 and 6, RMSE at fixed gene frequency)

Published comparison; transcribed, not reproduced.

Evaluation results

1 evaluation · 3 results. Different protocols are not a single leaderboard.

Protocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency · Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)

Sorted by RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average) (lower is better). The best value in each column is highlighted. Decimals are rounded for display; each value links to the printed value and its source.

Tested configurationRMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average)RMSE (test RMSE of pair viability, training set with each gene seen about 20 times on average)RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average)
MLP on summed gene embeddings, trained once on a downsampled setEvaluation6.3 (1.1)5.9 (0.1)7.8 (2.5)
All 3 result rows with coverage, uncertainty and sources
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Exact evaluated configurations and original reported results
Tested configurationProtocol and datasetFindingEvidence and details
Configuration: MLP on summed gene embeddings, trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)
6.3 (1.1) RMSE (test RMSE of pair viability, training set with each gene seen about 10 times on average)
unitless · lower

Uncertainty: SE 1.1. Caption: error is SE across three cross-fold replicates

Coverage: Not reported scored / Not reported eligible

Author-reported evaluation · Source checked
Methods, coverage and source

MLP on summed gene embeddings, trained once on a downsampled set on Horlbeck K562 (Tables 1 and 6, RMSE at fixed gene frequency)

pheno-20261022-qin2025-protocol-downsampling-rmse

Aggregation: Not reported

Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 6, Gene Frequency 10 block, row "MLP", column "Horlbeck K562"
Configuration: MLP on summed gene embeddings, trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)
5.9 (0.1) RMSE (test RMSE of pair viability, training set with each gene seen about 20 times on average)
unitless · lower

Uncertainty: SE 0.1. Caption: error is SE across three cross-fold replicates

Coverage: Not reported scored / Not reported eligible

Author-reported evaluation · Source checked
Methods, coverage and source

MLP on summed gene embeddings, trained once on a downsampled set on Horlbeck K562 (Tables 1 and 6, RMSE at fixed gene frequency)

pheno-20261022-qin2025-protocol-downsampling-rmse

Aggregation: Not reported

Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 20 block, row "MLP", column "Horlbeck K562"
Configuration: MLP on summed gene embeddings, trained once on a downsampled setProtocol: Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset: Horlbeck et al. 2018 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)
7.8 (2.5) RMSE (test RMSE of pair viability, training set with each gene seen about 4 times on average)
unitless · lower

Uncertainty: SE 2.5. Caption: error is SE across three cross-fold replicates

Coverage: Not reported scored / Not reported eligible

Author-reported evaluation · Source checked
Methods, coverage and source

MLP on summed gene embeddings, trained once on a downsampled set on Horlbeck K562 (Tables 1 and 6, RMSE at fixed gene frequency)

pheno-20261022-qin2025-protocol-downsampling-rmse

Aggregation: Not reported

Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267) · Table 1, Gene Frequency 4 block, row "MLP", column "Horlbeck K562"

Source checking is not independent reproduction. Release 2026-10-23-5347695500ed.

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Release 2026-10-23-5347695500ed · Evidence verified: Not verified

Evidence incomplete

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  • Score meaning and direction are confirmed: not yet verified
  • Metrics are recomputed from the saved predictions: not yet verified

Verified: Not verified

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Investigate discrepancies

Replay evidence includes annotations and an assessment of dependence. Unknown independence permits descriptive analysis only.

Missing or unresolved evidence

  • No verified artifact manifest is linked to this exact record.
  • File checksums match the recorded files: not yet verified
  • Predictions are matched to the right samples: not yet verified
  • Score meaning and direction are confirmed: not yet verified
  • Metrics are recomputed from the saved predictions: not yet verified
  • Sample annotations are recorded: not yet verified
  • Dependence between samples is assessed: not yet verified

Verified: Not verified

Evidence incomplete

Run locally

A pinned recipe describes the inputs, environment and resource requirements.

Missing or unresolved evidence

  • No verified artifact manifest is linked to this exact record.
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  • Score meaning and direction are confirmed: not yet verified
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Verified: Not verified

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Missing or unresolved evidence

  • No verified artifact manifest is linked to this exact record.
  • File checksums match the recorded files: not yet verified
  • Predictions are matched to the right samples: not yet verified
  • Score meaning and direction are confirmed: not yet verified
  • Independent validation data exist: not yet verified
  • Overlap with training data is checked: not yet verified

Verified: Not verified

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Artifacts and reproduction

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Evaluation procedure

Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency

Configuration
MLP on summed gene embeddings, trained once on a downsampled set
Protocol
Combination phenotype prediction from small training sets: test RMSE at fixed average gene frequency
Dataset
Horlbeck et al. 2018 combinatorial CRISPRi in K562, gene-level viability (as processed by Qin et al. 2025)
origin
Author-reported evaluation
configuration
Qin et al. 2025, PMLR 267 proceedings PDF
protocol id
pheno-20261022-qin2025-protocol-downsampling-rmse
dataset version
Gene-level viability as processed in Qin et al. 2025 Appendix A.1
split
Downsampled training set; 10% validation and 30% test
population
100,576 two-gene combinations of 448 genes
inputs
Single-gene viability effects and the measured viability of the training pairs
adaptation
MLP trained once on the downsampled set
metric implementation
RMSE of predicted pair viability on the test set
aggregation
Three cross-fold replicates with standard error printed; the caption does not name the point statistic
budget
Not reported

Metadata review: source checked. Unreported conditions prevent automatic comparisons.

Reproduction

Split
Downsampled training set; 10% validation and 30% test
Adaptation
MLP trained once on the downsampled set
Scoring implementation
RMSE of predicted pair viability on the test set

No execution recipe has been verified for this exact configuration and evaluation. A benchmark's general instructions may use different inputs, splits or model settings.

Reproducing this published result requires matching its model configuration, data, split and scorer. Source checking or a successful smoke test does not establish score reproduction.

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.

21 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-10-23-5347695500ed
Property and statementOriginal source and locationReview and provenance
Comparison: adaptation
MLP trained once on the downsampled set
Context-only references
Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Table 1 and Table 6, row 'MLP', column 'Horlbeck K562'

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

author reported

Audit details

Field: attributes.comparison.adaptation

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Comparison: aggregation
Three cross-fold replicates with standard error printed; the caption does not name the point statistic
Context-only references
Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Table 1 and Table 6, row 'MLP', column 'Horlbeck K562'

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

author reported

Audit details

Field: attributes.comparison.aggregation

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Comparison: budget
Not reported
Context-only references
Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Table 1 and Table 6, row 'MLP', column 'Horlbeck K562'

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

missing or unspecified

No individual claim review recorded

author reported

Audit details

Field: attributes.comparison.budget

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Comparison: dataset version
Gene-level viability as processed in Qin et al. 2025 Appendix A.1
Context-only references
Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Table 1 and Table 6, row 'MLP', column 'Horlbeck K562'

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

author reported

Audit details

Field: attributes.comparison.dataset_version

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Comparison: inputs
Single-gene viability effects and the measured viability of the training pairs
Context-only references
Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Table 1 and Table 6, row 'MLP', column 'Horlbeck K562'

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

author reported

Audit details

Field: attributes.comparison.inputs

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Comparison: metric implementation
RMSE of predicted pair viability on the test set
Context-only references
Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Table 1 and Table 6, row 'MLP', column 'Horlbeck K562'

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

author reported

Audit details

Field: attributes.comparison.metric_implementation

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Comparison: population
100,576 two-gene combinations of 448 genes
Context-only references
Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Table 1 and Table 6, row 'MLP', column 'Horlbeck K562'

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

author reported

Audit details

Field: attributes.comparison.population

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Comparison: protocol id
pheno-20261022-qin2025-protocol-downsampling-rmse
Context-only references
Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Table 1 and Table 6, row 'MLP', column 'Horlbeck K562'

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

author reported

Audit details

Field: attributes.comparison.protocol_id

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Comparison: split
Downsampled training set; 10% validation and 30% test
Context-only references
Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Table 1 and Table 6, row 'MLP', column 'Horlbeck K562'

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

author reported

Audit details

Field: attributes.comparison.split

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

Evaluation group id
qin2025-table1-6
Context-only references
Qin et al. 2025, Active Learning for Efficient Discovery of Optimal Combinatorial Perturbations (ICML 2025, PMLR 267)

Original source ↗

Table 1 and Table 6, row 'MLP', column 'Horlbeck K562'

Version: Proceedings of the 42nd ICML, PMLR 267:50363-50383, 2025; proceedings PDF qin25g.pdf. The arXiv preprint 2411.12010 was not read.
Retrieved: 2026-10-22T13:57:27Z

not individually reviewed

No individual claim review recorded

author reported

Audit details

Field: attributes.evaluation_group_id

Source artifact SHA-256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb

Hash scope: pdftotext -layout text layer, parsed by extract/qin2025naiad.py

Inspected artifact

How to read the evidence

Sources and history

Release 2026-10-23-5347695500ed · Record review: source checked

1 source record and release historyDownload this release (gzip)
Technical metadata and extraction receipts

Stable ID: pheno-20261022-qin2025-eval-table1-6-mlp-gene-embedding-prediction-horlbeck2018-k562

areas
cells-tissues
contexts
research
origin
author_reported
protocol
pheno-20261022-qin2025-protocol-downsampling-rmse
version
Qin et al. 2025, PMLR 267 proceedings PDF
comparison
protocol id: pheno-20261022-qin2025-protocol-downsampling-rmse; dataset version: Gene-level viability as processed in Qin et al. 2025 Appendix A.1; split: Downsampled training set; 10% validation and 30% test; population: 100,576 two-gene combinations of 448 genes; inputs: Single-gene viability effects and the measured viability of the training pairs; adaptation: MLP trained once on the downsampled set; metric implementation: RMSE of predicted pair viability on the test set; aggregation: Three cross-fold replicates with standard error printed; the caption does not name the point statistic; budget: Not reported
evaluation group id
qin2025-table1-6
evaluation group name
Tables 1 and 6, RMSE at fixed gene frequency
retrospective
true
source locator
Table 1 and Table 6, row 'MLP', column 'Horlbeck K562'
review
method: source-hash-verification; scope-review; independent-cell-check; reviewer: claude; reviewer note: Claude (Opus 5.5) review agent, independent of the extraction agent; no human review claimed; date: 2026-10-22; artifact sha256: 983b2da8aed78f0aced27053301c15f0f67ebd0fac706435bb6800e309dd64eb; retrieval url: https://raw.githubusercontent.com/mlresearch/v267/main/assets/qin25g/qin25g.pdf; method note: Re-hashed the PDF in the scratchpad (SHA-256 matches the source record). Read the captions and methods text in the pdftotext layer and on the rendered pages.; note: Origin, comparison fields, group, locator and links checked against the table caption, Sections 4.2, 4.3 and 5.3 and Appendices B to D.
missing metadata
budget: reason: inapplicable; note: Single training run on a fixed subsample; no acquisition budget.
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