Evidence claim

Baseline: Select therapeutic targets for validation

Baseline of Select therapeutic targets for validation

Random and pathway-based selection

Baseline method for the use case, reviewed against its cited sources.

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.

18 evidence rows matching the loaded filters

Claims, original sources and review scope · Release 2026-11-02-a7f03c9e55a5
Property and statement	Original source and location	Review and provenance
Claimed field baseline Context-only references	LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? Original source ↗ tgtval-20261009-source-roohani2025: Appendix (human pathway baseline) and Table 6 (pathways), Table 2 (Random); tgtval-20261009-source-gupta2025: baselines description and Table 3 (Random) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15482-15510 Retrieved: 2026-10-09T20:55:36Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `7dda0b590f2736b7d30e48b97167a0868a2b4bde253589d8aff7929d01506ff9` Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py Inspected artifact
Claimed field baseline Context-only references	BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments Original source ↗ tgtval-20261009-source-roohani2025: Appendix (human pathway baseline) and Table 6 (pathways), Table 2 (Random); tgtval-20261009-source-gupta2025: baselines description and Table 3 (Random) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv:2405.17631 version 3, updated 2025-03-09; published as a conference paper at ICLR 2025 Retrieved: 2026-10-09T20:51:27Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.field` Source artifact SHA-256: `dd94d80ec75c9bb84ec3989c910a313ef1a7aec57c772d3dd35ecadd250132a7` Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py Inspected artifact
Method ids 2 values tgtval-20261009-method-random-selection tgtval-20261009-method-human-pathway-selection Context-only references	LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? Original source ↗ tgtval-20261009-source-roohani2025: Appendix (human pathway baseline) and Table 6 (pathways), Table 2 (Random); tgtval-20261009-source-gupta2025: baselines description and Table 3 (Random) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15482-15510 Retrieved: 2026-10-09T20:55:36Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.method_ids` Source artifact SHA-256: `7dda0b590f2736b7d30e48b97167a0868a2b4bde253589d8aff7929d01506ff9` Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py Inspected artifact
Method ids 2 values tgtval-20261009-method-random-selection tgtval-20261009-method-human-pathway-selection Context-only references	BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments Original source ↗ tgtval-20261009-source-roohani2025: Appendix (human pathway baseline) and Table 6 (pathways), Table 2 (Random); tgtval-20261009-source-gupta2025: baselines description and Table 3 (Random) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv:2405.17631 version 3, updated 2025-03-09; published as a conference paper at ICLR 2025 Retrieved: 2026-10-09T20:51:27Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.method_ids` Source artifact SHA-256: `dd94d80ec75c9bb84ec3989c910a313ef1a7aec57c772d3dd35ecadd250132a7` Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py Inspected artifact
Pinned records 3 values record id tgtval-20261009-source-gupta2025 fields status attributes.artifact_sha256 sha256 6f5ca86baa095c19fc137331fb00935d89468ace1de74510fee46e8dba5971a7 record id tgtval-20261009-source-roohani2025 fields status attributes.artifact_sha256 sha256 2cd7fb822f68bd61204618ad8ffaf1f70dfb094237c5787671c3a3ecfc8afd5b record id use-case-therapeutic-target-validation fields status name description facets attributes.question attributes.decision attributes.inputs attributes.output attributes.exclusions attributes.setting attributes.clinical_scope attributes.intended_users sha256 3b7601f2decfe5a3304ea2c8ccaa89929148f2d443b05bd6c9e92f1844c0a578 Context-only references	LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? Original source ↗ tgtval-20261009-source-roohani2025: Appendix (human pathway baseline) and Table 6 (pathways), Table 2 (Random); tgtval-20261009-source-gupta2025: baselines description and Table 3 (Random) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15482-15510 Retrieved: 2026-10-09T20:55:36Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.pins` Source artifact SHA-256: `7dda0b590f2736b7d30e48b97167a0868a2b4bde253589d8aff7929d01506ff9` Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py Inspected artifact
Pinned records 3 values record id tgtval-20261009-source-gupta2025 fields status attributes.artifact_sha256 sha256 6f5ca86baa095c19fc137331fb00935d89468ace1de74510fee46e8dba5971a7 record id tgtval-20261009-source-roohani2025 fields status attributes.artifact_sha256 sha256 2cd7fb822f68bd61204618ad8ffaf1f70dfb094237c5787671c3a3ecfc8afd5b record id use-case-therapeutic-target-validation fields status name description facets attributes.question attributes.decision attributes.inputs attributes.output attributes.exclusions attributes.setting attributes.clinical_scope attributes.intended_users sha256 3b7601f2decfe5a3304ea2c8ccaa89929148f2d443b05bd6c9e92f1844c0a578 Context-only references	BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments Original source ↗ tgtval-20261009-source-roohani2025: Appendix (human pathway baseline) and Table 6 (pathways), Table 2 (Random); tgtval-20261009-source-gupta2025: baselines description and Table 3 (Random) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv:2405.17631 version 3, updated 2025-03-09; published as a conference paper at ICLR 2025 Retrieved: 2026-10-09T20:51:27Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.pins` Source artifact SHA-256: `dd94d80ec75c9bb84ec3989c910a313ef1a7aec57c772d3dd35ecadd250132a7` Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py Inspected artifact
Rationale There is no pre-machine-learning method for choosing which targets to perturb; the established comparators are random selection and genes chosen from relevant pathways. Agent and Bayesian optimisation methods are judged by how many more hits they find than these. Context-only references	LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? Original source ↗ tgtval-20261009-source-roohani2025: Appendix (human pathway baseline) and Table 6 (pathways), Table 2 (Random); tgtval-20261009-source-gupta2025: baselines description and Table 3 (Random) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15482-15510 Retrieved: 2026-10-09T20:55:36Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.rationale` Source artifact SHA-256: `7dda0b590f2736b7d30e48b97167a0868a2b4bde253589d8aff7929d01506ff9` Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py Inspected artifact
Rationale There is no pre-machine-learning method for choosing which targets to perturb; the established comparators are random selection and genes chosen from relevant pathways. Agent and Bayesian optimisation methods are judged by how many more hits they find than these. Context-only references	BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments Original source ↗ tgtval-20261009-source-roohani2025: Appendix (human pathway baseline) and Table 6 (pathways), Table 2 (Random); tgtval-20261009-source-gupta2025: baselines description and Table 3 (Random) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv:2405.17631 version 3, updated 2025-03-09; published as a conference paper at ICLR 2025 Retrieved: 2026-10-09T20:51:27Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.rationale` Source artifact SHA-256: `dd94d80ec75c9bb84ec3989c910a313ef1a7aec57c772d3dd35ecadd250132a7` Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py Inspected artifact
Source location tgtval-20261009-source-roohani2025: Appendix (human pathway baseline) and Table 6 (pathways), Table 2 (Random); tgtval-20261009-source-gupta2025: baselines description and Table 3 (Random) Context-only references	LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? Original source ↗ tgtval-20261009-source-roohani2025: Appendix (human pathway baseline) and Table 6 (pathways), Table 2 (Random); tgtval-20261009-source-gupta2025: baselines description and Table 3 (Random) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15482-15510 Retrieved: 2026-10-09T20:55:36Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.source_locator` Source artifact SHA-256: `7dda0b590f2736b7d30e48b97167a0868a2b4bde253589d8aff7929d01506ff9` Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py Inspected artifact
Source location tgtval-20261009-source-roohani2025: Appendix (human pathway baseline) and Table 6 (pathways), Table 2 (Random); tgtval-20261009-source-gupta2025: baselines description and Table 3 (Random) Context-only references	BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments Original source ↗ tgtval-20261009-source-roohani2025: Appendix (human pathway baseline) and Table 6 (pathways), Table 2 (Random); tgtval-20261009-source-gupta2025: baselines description and Table 3 (Random) Shared locator for this statement’s cited sources; not a separate locator for each citation. Version: arXiv:2405.17631 version 3, updated 2025-03-09; published as a conference paper at ICLR 2025 Retrieved: 2026-10-09T20:51:27Z	not individually reviewed No individual claim review recorded Audit details Field: `attributes.source_locator` Source artifact SHA-256: `dd94d80ec75c9bb84ec3989c910a313ef1a7aec57c772d3dd35ecadd250132a7` Hash scope: pdftotext -layout text layer, parsed by extract/extract_target_validation.py Inspected artifact

How to read the evidence

Sources and history

Release 2026-11-02-a7f03c9e55a5 · Record review: source checked

2 source records and release history

BioDiscoveryAgent: An AI Agent for Designing Genetic Perturbation Experiments · Original source · arXiv:2405.17631 version 3, updated 2025-03-09; published as a conference paper at ICLR 2025
LLMs for Bayesian Optimization in Scientific Domains: Are We There Yet? · Original source · Findings of the Association for Computational Linguistics: EMNLP 2025, pages 15482-15510

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Technical metadata and extraction receipts

Stable ID: use-case-baseline-therapeutic-target-validation

field: baseline
value: Random and pathway-based selection
source locator: tgtval-20261009-source-roohani2025: Appendix (human pathway baseline) and Table 6 (pathways), Table 2 (Random); tgtval-20261009-source-gupta2025: baselines description and Table 3 (Random)
method ids: tgtval-20261009-method-random-selection; tgtval-20261009-method-human-pathway-selection
rationale: There is no pre-machine-learning method for choosing which targets to perturb; the established comparators are random selection and genes chosen from relevant pathways. Agent and Bayesian optimisation methods are judged by how many more hits they find than these.
review: method: source-hash-verification; automated-source-review; reviewer: claude; reviewer note: Separate Claude review agent, independent of the agent that proposed the baseline; no human review claimed; reviewed at: 2026-10-12T18:00:00Z; note: Accepted as proposed. Sources re-retrieved and read; method records and their configuration links checked against the use case's active comparisons. Names the reference newer methods are compared with, not a recommendation. See docs/reviews/use-cases/baselines-2026-10-12.md.
pins: record id: tgtval-20261009-source-gupta2025; fields: status; attributes.artifact_sha256; sha256: 6f5ca86baa095c19fc137331fb00935d89468ace1de74510fee46e8dba5971a7; record id: tgtval-20261009-source-roohani2025; fields: status; attributes.artifact_sha256; sha256: 2cd7fb822f68bd61204618ad8ffaf1f70dfb094237c5787671c3a3ecfc8afd5b; record id: use-case-therapeutic-target-validation; fields: status; name; description; facets; attributes.question; attributes.decision; attributes.inputs; attributes.output; attributes.exclusions; attributes.setting; attributes.clinical_scope; attributes.intended_users; sha256: 3b7601f2decfe5a3304ea2c8ccaa89929148f2d443b05bd6c9e92f1844c0a578

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