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Auditing Retrieval-Augmented LLM Hypotheses for Longitudinal Cell Painting Morphology

Year: 2026

Type: conference-paper

Abstract: High-content morphological profiling (Cell Painting) yields sensitive, high-dimensional signatures of cellular state, but translating longitudinal morphology trajectories into interpretable biology remains difficult—especially for weak, chronic perturbations such as low-dose-rate ionizing radiation. Large language models (LLMs) can synthesize heterogeneous evidence into narratives, yet scientific use requires quantitative auditing: fluent outputs may be ungrounded or inconsistent with measured morphology.

Source: ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

DOI: 10.1145/3807503.3819448

Authors: Gilchan Park, Guang Zhao, Byung-Jun Yoon, Shinjae Yoo

Institution: Brookhaven National Laboratory

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: U.S. Department of Energy, Office of Science, Biological and Environmental Research

Awards: G2287611682, G262962599, G2857925935, G498139845, G6848031779, G6918803902, G969889393, G993149817

OpenAlex ID: W7170496102

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