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

TL;DR

This paper introduces FREEFORM, a knowledge-driven framework leveraging large language models for feature selection and engineering in genotype data, improving phenotype prediction especially in low-data scenarios.

Contribution

The paper presents a novel framework that uses pre-trained LLMs for knowledge-driven feature selection and engineering in genotype data, outperforming traditional data-driven methods.

Findings

Outperforms data-driven methods on genotype-phenotype prediction tasks

Effective in low-shot regimes with limited data

Open-source implementation available at GitHub

Abstract

Predicting phenotypes with complex genetic bases based on a small, interpretable set of variant features remains a challenging task. Conventionally, data-driven approaches are utilized for this task, yet the high dimensional nature of genotype data makes the analysis and prediction difficult. Motivated by the extensive knowledge encoded in pre-trained LLMs and their success in processing complex biomedical concepts, we set to examine the ability of LLMs in feature selection and engineering for tabular genotype data, with a novel knowledge-driven framework. We develop FREEFORM, Free-flow Reasoning and Ensembling for Enhanced Feature Output and Robust Modeling, designed with chain-of-thought and ensembling principles, to select and engineer features with the intrinsic knowledge of LLMs. Evaluated on two distinct genotype-phenotype datasets, genetic ancestry and hereditary hearing loss, we find this framework outperforms several data-driven methods, particularly on low-shot regimes. FREEFORM is available as open-source framework at GitHub: https://github.com/PennShenLab/FREEFORM.