Select perturbations for a defined cellular response
Which genetic perturbations should I test to produce a defined cellular response?
Evidence collection plan
Collection plannedEvidence collection is planned for this question. The plan defines a comparison to investigate; it does not establish model performance or suitability.
Comparison question
Does a model select more perturbations producing a prespecified phenotype than simple controls at the same test budget in the intended held-out setting?
Baselines to include
- Random candidate selection and mean-response/no-change controls
- Linear or other simple statistical response models
- Nearest measured perturbation
Outcomes to measure
- Experimentally confirmed phenotype hits per test budget
- Perturbation-specific expression signal as an intermediate endpoint
- Coverage and uncertainty for unseen interventions or contexts
Validation requirements
- Use the same candidate universe, phenotype definition and budget; specify how tied rankings are sampled.
- Separate unseen genes, combinations and cell contexts, holding out biological and experimental units.
- Audit guide efficacy, shared controls, batch effects and model training overlap.
- Do not choose evaluation genes using held-out responses; keep information-gain design separate.
Next collection task
Inventory perturbation studies with control and split metadata, separating confirmed phenotype-selection outcomes from expression-only metrics.
Your decision and inputs
Allocate a fixed experimental budget to perturbations most likely to produce the prespecified phenotype.
- Who this is for
- Functional genomics screen designers; Cell biologists planning perturbation experiments
- Context
- Research
- Inputs
- A defined cell system, starting state and target phenotype
- Candidate genetic perturbations and a fixed testing budget
- Measured perturbations and matched controls with guide, replicate and condition metadata
- The intended transfer setting: unseen genes, combinations or a new cellular context
- Expected output
- A ranked experimental shortlist with predicted phenotype effects, coverage and uncertainty about unmeasured conditions.
- Biological setting
- Research selection for a defined genetic-perturbation screen. Chemical interventions and combinations need their own protocols. This question concerns phenotype hits rather than expression reconstruction alone.
Outside this use case
- Transcriptome similarity alone does not establish successful experimental selection.
- Generalisation to an unseen gene, combination and cell context must be evaluated separately.
- Choosing experiments to maximise information gain is a separate decision.
What this establishes for clinical research
Research only. Predicted cellular responses do not establish patient response or treatment-selection validity.
Which evaluations inform this question?
No model comparison has been collected for this question yet.
Relevant methods and studies may exist outside this collection.
What evidence is still missing?
- Fixed-budget experimental hit-selection comparisons have not yet been collected for this question.
- Expression benchmarks may provide only intermediate evidence for the chosen phenotype.
- Human domain review remains unassigned.
Sources and review
Automated source review · 2026-09-28 · Codex
Automated review of the workflow definition, cited primary-source scope and collection plan. No model evaluation or applicability mapping was added. Human domain review remains unassigned.
- Rewire research use-case priorities: workflow definitions and evidence plans · Original source ↗
R3 — Phenotype-driven perturbation selection
Release provenance and downloads
Release 2026-09-28-c7b5ac6d34f2
Use-case input digest a0dd27a5f430ec387d309fd6e8615083250873ce4cff5e882c6fb8458ef80e95
Download questions, collection plans and review metadata (JSON) · Verify release checksums
Question use-case-phenotype-perturbation-selection. Any numerical results on this page come from this release's existing evaluation records.