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Updated: May 24, 2025

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作为基因分析OpenAI模型的替代品的小型开源文本嵌入模型

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

概括

开源文本嵌入模型为基因表达分析专有解决方案提供了经济有效和高效的替代方案. 这些模型在基因分类任务中显示了可比或优异的性能,而无需进行广泛的微调.

科学领域:

生物信息学是一种生物信息学.
计算生物学计算生物学
基因组学就是基因组学.

背景情况:

用于基因表达分析的基础变压器模型在计算上昂贵.
GenePT使用OpenAI的文本嵌入来进行基因信息编码.
开放AI的封闭源代码模型引发了数据隐私方面的担忧.

研究的目的:

调查基于开源变压器的文本嵌入模型,作为 OpenAI 服务的替代方案.
评估用于基因表达数据分析的轻量级开源模型的性能.

主要方法:

识别了十个小型,计算轻的变压器模型从拥抱脸.
在四个不同的基因分类任务中评估模型.
评估了微调对模型性能的影响.

主要成果:

几种开源模型与OpenAI的性能相匹配或超过.
模型大小和计算轻度是关键的选择标准.
微调并不总是产生显著的性能改善.

结论:

开源文本嵌入模型是基因表达分析的可行,高效和潜在的优越替代方案.
这些模型减轻了与封闭源解决方案相关的数据隐私问题.
为了达到高性能,往往不需要进一步的微调.

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