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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

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Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models.

Joseph Lee¹, Shu Yang¹, Jae Young Baik^1,2,3,4,5

¹Unversity of Pennsylvania, Philadelphia, USA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science

|June 12, 2025

Summary

This summary is machine-generated.

This study introduces FREEFORM, a novel framework using large language models (LLMs) for genetic feature selection. FREEFORM enhances phenotype prediction from genotype data, especially in low-data scenarios.

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Published on: October 11, 2018

Area of Science:

Genomics
Bioinformatics
Artificial Intelligence

Background:

Predicting complex phenotypes from genetic data is challenging due to high-dimensional genotype information.
Conventional data-driven methods struggle with the complexity and scale of genetic datasets.
Large Language Models (LLMs) offer potential for leveraging biomedical knowledge in genetic analysis.

Purpose of the Study:

To investigate the capability of LLMs in feature selection and engineering for tabular genotype data.
To develop a novel knowledge-driven framework for improved phenotype prediction.
To assess the performance of LLM-based approaches against traditional methods.

Main Methods:

Developed FREEFORM (Free-flow Reasoning and Ensembling for Enhanced Feature Output and Robust Modeling), a framework utilizing LLMs.
Incorporated chain-of-thought and ensembling principles within the LLM framework.
Applied the framework to genotype-phenotype datasets for genetic ancestry and hereditary hearing loss.

Main Results:

FREEFORM demonstrated superior performance compared to several data-driven methods.
The framework showed particular effectiveness in low-data regimes.
LLMs successfully performed feature selection and engineering for genotype data.

Conclusions:

LLMs can be effectively utilized for feature selection and engineering in genetic studies.
The FREEFORM framework offers a promising knowledge-driven approach for phenotype prediction.
This method shows potential for advancing genetic analysis, especially with limited data.