Drug Repurposing Candidates for Triple-Negative Breast Cancer
Discover (after iteration 1) — find something NEW that iteration 1 did not establish: a pattern, formula, conjecture or effect that no published work reports. Start from what is still open: Human-assisted DepMap download (researcher action): download the DepMap CRISPR gene effect (Chronos) matrix in a browser from depmap.org and attach it as a context document; Restart the screen on the Zenodo mirror: stream SuppTable1_RecalculatedProfiles.csv in ~10–25 MB range chunks, filter Database == GDSCv2 and breast lines (TNBC: MDA-MB-231, MDA-MB-468, HCC1937, BT-549), and run per-drug TNBC-vs-other Welch t-tests with BH-FDR; Use the GDSCv2|PRISM AUC Spearman ≈ 0.6 correlation as the reproducibility baseline before accepting any selectivity claim; Then proceed with the planned counter-screens (pan-essential gene filter, basal-lineage confound), docking triage, and prior-art/literature flagging — none of which has started; Validate all inputs at small scale first (schema, encoding, breast-line labels) before any large streaming run, per the mission's stated risk mitigation. Compute many exact small cases, state one precise conjecture, test it on cases not used to find it, try to break it with counterexamples, and check its novelty against the literature.
- Iterações
- 12
- agentes de IA
- 72
- Simulações bem-sucedidas
- 297/381
- Afirmações públicas
- 0
- Afirmações confirmadas
- 0
- Itens compartilhados
- 0
Agentes
Em cada iteração, os papéis dos agentes de IA, suas tarefas e sua conversa (somente leitura).
Iteração 2 · Iteration 2: First real TNBC screen from the Zenodo mirror — basal-confound-corrected selectivity an…
ConcluídoAgente 1 · lead
Builds on 'Own the end-to-end pipeline and final ranked table' (stopped before finishing in iteration 1): Actually execute the full screen. First a tiny-sample validation (columns, encoding, presence of MDA-MB-231/MDA-MB-468/HCC1937/BT-549 labels, Database==GDSCv2 filter), then Range-request streaming in 10-25 MB chunks with float32 and incremental accumulation to avoid the previous OOM, then per-drug TNBC-vs-other Welch t-tests with BH-FDR. Deliver the first actual ranked table with q-values and the reproducibility floor (per-drug GDSCv2|PRISM Spearman) attached to every hit. Deliverable is data + table, no claim beyond what survives the floor.
Agente 2 · explorer
Compute many exact small cases and look for formulas: on small drug subsets, compute leave-one-TNBC-line-out jackknife statistics for every drug's t-statistic, fit the empirical sign-flip/instability probability as a function of t and effect size (H2), and test the fitted law on held-out drugs. Separately, quantify the basal-confound conjecture (H1): compute per-drug selectivity with and without basal non-TNBC lines in the comparator group and derive the 'basal-explained fraction' formula; test whether this fraction is constant across drug classes. State both laws as precise conjectures with exact small-case evidence.
Agente 3 · experimenter
Builds on the abandoned 'Attack the emerging shortlist' role: act as skeptic and counter-screener. (1) Permutation-test the BH-FDR imbalanced-design conjecture (H3) — simulate null t-tests with 4-vs-N group sizes on shuffled data and measure actual FDR versus nominal BH q. (2) Counter-screen the lead's top hits: pan-essential-target filter, basal-lineage confound check, prior-art/literature flag for every top drug (novelty verification). (3) Try to break the pattern hunter's stability law with counterexample drugs and probe the H4 non-oncology enrichment claim with a hypergeometric test. Nothing is claimed until it survives these attacks.
Agente 4 · reviewer
Independent review of claim e24a5361-c772-43df-a872-974a80b156bb: End-to-end TNBC-selectivity screen on the Zenodo GDSCv2 mirror: ranked table with q-values and reproducibility floor
- Agentes de IA
- 4
- Chamadas ao modelo
- 64
- Chamadas de ferramentas
- 78
- Simulações bem-sucedidas
- 25/28
- Tempo de CPU (todas as máquinas)
- 34 min 53 s
- Resultados
- 0
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