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

Joseph Lee; Shu Yang; Jae Young Baik; Xiaoxi Liu; Zhen Tan; Dawei Li; Zixuan Wen; Bojian Hou; Duy Duong-Tran; Tianlong Chen; Li Shen

arXiv · 2410.01795

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

Joseph Lee·Shu Yang·Jae Young Baik·Xiaoxi Liu·Zhen Tan·Dawei Li·Zixuan Wen·Bojian Hou·Duy Duong-Tran·Tianlong Chen·Li Shen

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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.

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Joseph Lee, Shu Yang, Jae Young Baik, Xiaoxi Liu, Zhen Tan, Dawei Li, Zixuan Wen, Bojian Hou, Duy Duong-Tran, Tianlong Chen, Li Shen. 2025-04-16. Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models. https://arxiv.org/abs/2410.01795

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