The Gene Scene

GeneGround is a claim-level grounding layer for AI-generated omics interpretations. I built it to address a core problem in AI-assisted biology: LLMs can generate fluent scientific narratives, but they may subtly overstate causality, mechanism, robustness, or biological scope. GeneGround takes an AI-generated omics interpretation and evidence handoff file, then decomposes the interpretation into individual biological claims. It normalizes genes, pathways, cell contexts, conditions, and directions against controlled taxonomies; indexes the handoff into perturbation, pathway, robustness, provenance, and language-rule evidence; retrieves relevant chunks using metadata-first retrieval with local TF-IDF fallback; and evaluates each claim with four specialist Claude-powered agents. Final verdicts are assigned through deterministic aggregation, while Claude is used for semantic biological judgment and grounded rewrites. The Technical Pipeline page exposes every processing stage from claim extraction through retrieval and verdict generation, while an interactive Evidence Trace links each sentence to its claims, agent queries, retrieved evidence chunks, and final reasoning. In the demo, GeneGround audited a primary human CD4+ T cell Perturb-seq interpretation, identifying claims supported with caveats, partially supported, or overstated. This matters because AI-assisted science needs traceable evidence, cautious language, and reproducible reasoning, not just plausible summaries.

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