pith:HSLQ6M3W
Contextual Invertible World Models: A Neuro-Symbolic Agentic Framework for Colorectal Cancer Drug Response
A neuro-symbolic framework integrates machine learning emulation with LLM reasoning to predict colorectal cancer drug responses and identify APC/Wnt pathway dominance.
arxiv:2603.02274 v3 · 2026-03-01 · q-bio.QM · cs.AI
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Claims
Utilising a zero-leakage forensic pipeline on the Sanger GDSC dataset (N = 83), we achieve a robust predictive correlation (r = 0.447, p = 2.30e-05). ... identifying a hierarchical dominance of the APC/Wnt-axis over the p53 apoptotic pathway. Validated against human clinical profiles (TCGA-COAD proxy, p = 0.0357)
The assumption that the LLM-based reasoning layer delivers genuine mechanistic insight rather than plausible post-hoc explanations, and that the small N=83 zero-leakage pipeline plus TCGA proxy validation generalizes beyond the specific dataset and model choices.
CIWM neuro-symbolic framework reports r=0.447 correlation on GDSC N=83 data for colorectal cancer drug response and finds APC/Wnt pathway dominance over p53 via in silico perturbations validated on TCGA.
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| First computed | 2026-06-12T01:08:23.433652Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
3c970f337672532da1ff15cd23d38741a1389f16442f24947859456646621381
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/HSLQ6M3WOJJS3IP7CXGSHU4HIG \
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Canonical record JSON
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