To evaluate the ability of large language models (LLMs) to identify diagnostic biomarkers from cell-free RNA (cfRNA) data.
Approach:
Study Design: Researchers assessed several LLMs using published cfRNA datasets from three patient cohorts with varying diagnostic complexities.
Comparison Methodology: LLM-generated gene panels were compared to randomly selected genes and panels derived from conventional differential expression analyses.
Key Findings:
LLM-selected gene panels outperformed random selections, indicating the models can identify biologically relevant candidates.
Performance was strongest in the tuberculosis dataset, with some LLM-generated panels performing similarly to traditional methods.
Models frequently selected genes related to immune and inflammatory pathways.
LLMs showed inconsistent performance in executing a complete biomarker discovery workflow compared to established machine learning approaches.
Interpretation:
Current performance of LLMs does not replace established methods.
Limitations:
Inconsistent adherence to instructions by LLMs.
Challenges with reproducibility of results.
Conclusion:
LLM-generated biomarker signatures require rigorous validation before clinical application and should be used alongside traditional bioinformatics methods.
The procedure was performed under a HOPE Act research protocol at an NYU Langone Health center the institution said is among the limited number of US transplant centers equipped and approved to perform HOPE lung transplants.
