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

• URL: http://arxiv.org/abs/2410.01795v1
• Date: Wed, 2 Oct 2024 17:53:08 GMT
• Title: Knowledge-Driven Feature Selection and Engineering for Genotype Data with Large Language Models
• Authors: Joseph Lee, Shu Yang, Jae Young Baik, Xiaoxi Liu, Zhen Tan, Dawei Li, Zixuan Wen, Bojian Hou, Duy Duong-Tran, Tianlong Chen, Li Shen,
• Abstract summary: We develop FREEFORM, Free-flow Reasoning and Ensembling for Enhanced Feature Output and Robust Modeling. FreeFORM is available as open-source framework at GitHub: https://github.com/PennShenLab/FREEFORM.
• Score: 35.084222907099644
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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