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

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Published on: December 6, 2024

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.

|May 5, 2025

Summary

This summary is machine-generated.

This study introduces FreeForm, a novel framework leveraging large language models (LLMs) for genetic feature selection and engineering. FreeForm enhances phenotype prediction from genotype data, outperforming traditional methods, especially with limited data.

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Published on: October 11, 2018

Area of Science:

Genomics
Bioinformatics
Artificial Intelligence in Medicine

Background:

Predicting complex genetic phenotypes from genotype data is challenging due to high dimensionality.
Current data-driven methods struggle with analysis and prediction accuracy.
Biomedical knowledge within pretrained large language models (LLMs) offers potential for genetics applications.

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 enhanced genotype-phenotype prediction.
To assess the performance of LLM-based feature engineering against data-driven approaches.

Main Methods:

Development of FreeForm (Free-flow Reasoning and Ensembling for Enhanced Feature Output and Robust Modeling).
Incorporation of chain-of-thought and ensembling principles within the LLM framework.
Evaluation on two distinct genotype-phenotype datasets: genetic ancestry and hereditary hearing loss.

Main Results:

The FreeForm framework demonstrates superior performance compared to several data-driven methods.
Performance gains are particularly notable in low-data regimes.
LLMs effectively contribute to feature selection and engineering for genotype data.

Conclusions:

FreeForm offers a promising knowledge-driven approach for genotype-phenotype prediction.
LLM-based feature engineering can overcome limitations of traditional data-driven methods.
The framework shows potential for improving genetic disease risk prediction and understanding complex genetic traits.