ArticlebioRxiv : the preprint server for biology2025
Incorporating LLM-Derived Information into Hypothesis Testing for Genomics Applications.
Article in bioRxiv : the preprint server for biology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
We propose strategies for incorporating the information in large language models (LLMs) into statistical hypothesis tests in genomics studies. Using gene embeddings derived from text inputs to OpenAI's GPT-3.5 model, we show that biological signals in a variety of genomics datasets reside near the principal subspace spanned by the embeddings. We then use a frequentist and Bayesian (FAB) framework to propose three hypothesis tests that are optimal with respect to prior information based on the gene embedding subspace. In three separate real-world genomics examples, the FAB tests guided by the LLM-derived information achieve more power than classical counterparts.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.