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Updated: May 24, 2025

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

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Small, Open-Source Text-Embedding Models as Substitutes to OpenAI Models for Gene Analysis.

Dailin Gan¹, Jun Li¹

¹Department of Applied and Computational Mathematics and Statistics, University of Notre Dame, Notre Dame, IN, USA.

Biorxiv : the Preprint Server for Biology

|March 3, 2025

Summary

This summary is machine-generated.

Open-source text-embedding models offer a cost-effective and efficient alternative to proprietary solutions for gene expression analysis. These models show comparable or superior performance in gene classification tasks without extensive fine-tuning.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Foundation transformer models for gene expression analysis are computationally expensive.
GenePT uses OpenAI's text-embedding for gene information encoding.
OpenAI's closed-source model raises data privacy concerns.

Purpose of the Study:

To investigate open-source transformer-based text-embedding models as alternatives to OpenAI's service.
To evaluate the performance of lightweight, open-source models for gene expression data analysis.

Main Methods:

Identified ten small, computationally light transformer models from Hugging Face.
Evaluated models across four distinct gene classification tasks.
Assessed the impact of fine-tuning on model performance.

Main Results:

Several open-source models matched or exceeded OpenAI's performance.
Model size and computational lightness were key selection criteria.
Fine-tuning did not consistently yield significant performance improvements.

Conclusions:

Open-source text-embedding models are viable, efficient, and potentially superior alternatives for gene expression analysis.
These models mitigate data privacy concerns associated with closed-source solutions.
Further fine-tuning is often unnecessary for achieving high performance.