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1 evaluation · 10 results. Different protocols are not a single leaderboard.
Protocol: AssayBench-Loop adaptive hit discovery: 10 rounds of 100 genes on 20 held-out CRISPR screens · Dataset subset: AssayBench-Loop test split (20 genome-wide CRISPR screens published after 2021)
Sorted by Hit enrichment factor (hits found divided by hits expected under random selection, after 10 rounds of 100 genes; hallucinated genes and unfilled slots count in the denominator, valid genes outside the screen library do not; mean over the 20 AssayBench-Loop test screens) (higher 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 configuration | Hit enrichment factor (hits found divided by hits expected under random selection, after 10 rounds of 100 genes; hallucinated genes and unfilled slots count in the denominator, valid genes outside the screen library do not; mean over the 20 AssayBench-Loop test screens) | Normalized cumulative hits AUC (area under the fraction-of-hits against fraction-of-library-queried curve over 10 rounds, divided by an oracle's area over the same effective budget; mean over the 20 AssayBench-Loop test screens) | Proportion (shortfall: share of acquired genes outside the screen library, including hallucinated gene names; mean over the 20 AssayBench-Loop test screens) | Recall (fraction of the screen's hits acquired within 10 rounds of 100 genes (FH); mean over the 20 AssayBench-Loop test screens) | Annotation overlap ratio (mean pairwise Jaccard overlap of GO biological-process annotations within a batch, divided by that of an equal-size random batch from the screen library, averaged over batches; mean over the 20 AssayBench-Loop test screens) | Effective number of groups (EP-B: Hill number of order 1 at batch scope, rarefied to 30 annotated genes per batch, one random group per gene per draw, 400 Monte Carlo draws, averaged within each screen then across the 20 test screens; not comparable with EP-S or EP-D) | Effective number of groups (EP-D: Hill number of order 1 at dataset scope, rarefied to 6,000 annotated genes across the complete test set, one random group per gene per draw, 300 Monte Carlo draws; not comparable with EP-B or EP-S) | Effective number of groups (EP-S: Hill number of order 1 at screen scope, rarefied to 200 annotated genes per screen, one random group per gene per draw, 400 Monte Carlo draws, averaged across the 20 test screens; not comparable with EP-B or EP-D) | Proportion (share of acquired hits that are DepMap common-essential genes (1,827 genes); mean over the 20 AssayBench-Loop test screens) | Vendi ratio (Vendi score of each batch under a cosine kernel on GenePT embeddings, divided by batch size and averaged over batches; mean over the 20 AssayBench-Loop test screens) |
|---|---|---|---|---|---|---|---|---|---|---|
| Screen-kNN (AssayBench-Loop)Evaluation | 3.4 | 12.5 | 3.7% | 17.3% | 2.91 | 18.9 | 54.8 | 42.8 | 77.1% | 57.6 |
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AssayBench-Loop adaptive hit discovery: 10 rounds of 100 genes on 20 held-out CRISPR screens
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19 evidence rows matching the loaded filters
| Property and statement | Original source and location | Review and provenance |
|---|---|---|
| Comparison: adaptation Method-specific; training on the 1,349 training screens permitted Context-only references | Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens Table 1, row 'Screen-kNN' (Adaptive Experimental Design Methods); Supplementary Table 4, row 'Screen-kNN' (Adaptive Experimental Design Methods) Version: arXiv:2609.11877 version 1, submitted 2026-09-10; not peer reviewed | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/edwards2026assayloop.py |
| Comparison: aggregation Per-screen metric averaged equally over the 20 test screens Context-only references | Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens Table 1, row 'Screen-kNN' (Adaptive Experimental Design Methods); Supplementary Table 4, row 'Screen-kNN' (Adaptive Experimental Design Methods) Version: arXiv:2609.11877 version 1, submitted 2026-09-10; not peer reviewed | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/edwards2026assayloop.py |
| Comparison: budget 10 rounds x 100 genes (1,000 acquisitions per screen) Context-only references | Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens Table 1, row 'Screen-kNN' (Adaptive Experimental Design Methods); Supplementary Table 4, row 'Screen-kNN' (Adaptive Experimental Design Methods) Version: arXiv:2609.11877 version 1, submitted 2026-09-10; not peer reviewed | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/edwards2026assayloop.py |
| Comparison: dataset version AssayBench-Loop test split, 20 screens (arXiv:2609.11877v1) Context-only references | Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens Table 1, row 'Screen-kNN' (Adaptive Experimental Design Methods); Supplementary Table 4, row 'Screen-kNN' (Adaptive Experimental Design Methods) Version: arXiv:2609.11877 version 1, submitted 2026-09-10; not peer reviewed | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/edwards2026assayloop.py |
| Comparison: inputs Screen description, genes acquired in earlier rounds with their hit labels, and the training screens Context-only references | Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens Table 1, row 'Screen-kNN' (Adaptive Experimental Design Methods); Supplementary Table 4, row 'Screen-kNN' (Adaptive Experimental Design Methods) Version: arXiv:2609.11877 version 1, submitted 2026-09-10; not peer reviewed | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/edwards2026assayloop.py |
| Comparison: metric implementation Methods 4.3 and Appendix H of arXiv:2609.11877v1 Context-only references | Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens Table 1, row 'Screen-kNN' (Adaptive Experimental Design Methods); Supplementary Table 4, row 'Screen-kNN' (Adaptive Experimental Design Methods) Version: arXiv:2609.11877 version 1, submitted 2026-09-10; not peer reviewed | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/edwards2026assayloop.py |
| Comparison: population 20 genome-wide CRISPR test screens; candidate pool of about 20,000 genes Context-only references | Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens Table 1, row 'Screen-kNN' (Adaptive Experimental Design Methods); Supplementary Table 4, row 'Screen-kNN' (Adaptive Experimental Design Methods) Version: arXiv:2609.11877 version 1, submitted 2026-09-10; not peer reviewed | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/edwards2026assayloop.py |
| Comparison: protocol id ucpheno-20261022-protocol-edwards2026-assaybench-loop-test-10x100 Context-only references | Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens Table 1, row 'Screen-kNN' (Adaptive Experimental Design Methods); Supplementary Table 4, row 'Screen-kNN' (Adaptive Experimental Design Methods) Version: arXiv:2609.11877 version 1, submitted 2026-09-10; not peer reviewed | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/edwards2026assayloop.py |
| Comparison: split Temporal: train before 2021 (1,349 screens), validation 2021 (20), test after 2021 (20) Context-only references | Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens Table 1, row 'Screen-kNN' (Adaptive Experimental Design Methods); Supplementary Table 4, row 'Screen-kNN' (Adaptive Experimental Design Methods) Version: arXiv:2609.11877 version 1, submitted 2026-09-10; not peer reviewed | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/edwards2026assayloop.py |
Limitations1 values
| Biology-in-the-loop: Amortized Adaptive Hit Discovery in CRISPR Screens Table 1, row 'Screen-kNN' (Adaptive Experimental Design Methods); Supplementary Table 4, row 'Screen-kNN' (Adaptive Experimental Design Methods) Version: arXiv:2609.11877 version 1, submitted 2026-09-10; not peer reviewed | not individually reviewed No individual claim review recorded author reported Audit detailsField: Source artifact SHA-256: Hash scope: pdftotext -layout text layer, parsed by data/omics/use-case-refresh-phenotype-20261022/extract/edwards2026assayloop.py |
Release 2026-10-23-5347695500ed · Record review: source checked
Stable ID: ucpheno-20261022-eval-edwards2026-screen-knn