Auditing Retrieval-Augmented LLM Hypotheses for Longitudinal Cell Painting Morphology

2024-04-09 · Source: cs.CL updates on arXiv.org · Field: Science & Research — Life Sciences & Biology, Computational Biology & Bioinformatics, Research Methodology & Innovation · Depth: Expert, extended

Summary

An evaluation-first, retrieval-augmented interpretation framework is introduced for longitudinal Cell Painting morphology, applied to a 9-week RPE-1 time course exposed to five low-dose-rate ionizing radiation levels (0.003–6.0 mGy/hr). This framework utilizes Google's Gemini model ("gemini-3-pro-preview") to synthesize quantitative morphology deltas with retrieved biological context from sources like Reactome and Europe PMC into structured, controlled-vocabulary hypotheses. A core innovation is the quantitative auditing process, featuring V1 citation validity to ensure all evidence identifiers are present in the prompt payload, and V2 proxy-based morphology compatibility, which assesses consistency between predicted biological processes and observed morphological changes. The audited run demonstrated perfect V1 validity and V2 compatibility that increased significantly with perturbation strength, ranging from 0.2361 at 0.003 mGy/hr to 0.8889 at 6.0 mGy/hr. This auditable pipeline successfully distilled complex cellular trajectories into falsifiable hypotheses, identifying a distinct adaptive phenotype at lower dose rates associated with metabolic reprogramming and proteostatic stress.

Key takeaway

For research scientists interpreting complex biological data with LLMs, you must integrate rigorous, quantitative auditing into your hypothesis generation workflows. Implement explicit validation layers like citation integrity (V1) and proxy-based compatibility (V2) to ensure outputs are grounded and consistent with measured data. This approach transforms LLM narratives into auditable scientific artifacts, crucial for reliable biological discovery, especially in scenarios lacking ground-truth labels.

Key insights

Auditable LLM frameworks can generate traceable, quantitatively validated biological hypotheses from complex morphological data.

Principles

• LLM outputs require quantitative auditing for scientific use.
• Hierarchical reasoning preserves temporal structure and provenance.
• Grounding integrity is necessary but not sufficient for faithfulness.

Method

An evaluation-first RAG framework uses Gemini to generate structured hypotheses from morphology deltas and retrieved evidence. Auditing involves V1 citation validity and V2 proxy-based morphology compatibility.

In practice

• Implement V1 checks for evidence ID presence in RAG.
• Define proxy mappings for process-morphology compatibility.
• Use hierarchical LLM reasoning for longitudinal data.

Topics

• Cell Painting
• LLM Auditing
• Retrieval-Augmented Generation
• Morphological Profiling
• Longitudinal Phenotyping
• Low-Dose Radiation

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.