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Updated: Jun 7, 2025

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从古典和基于机器学习的自然语言处理工具衍生出的蛋白质-蛋白质相互作用网络

David J Degnan¹, Clayton W Strauch², Moses Y Obiri³

¹Biological Sciences Division, Pacific Northwest National Laboratory, 902 Battelle Blvd, Richland, Washington 99354, United States.

Journal of proteome research

|November 11, 2024

概括

将蛋白质与蛋白质相互作用 (PPI) 的文本挖掘工具进行比较,可以发现权衡. 经典方法产生高的真正值但过度连接的网络,而机器学习和大型语言模型根据数据可用性提供不同的网络结构和性能.

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
自然语言处理自然语言处理.

背景情况:

蛋白与蛋白相互作用 (PPI) 对于理解生物机制至关重要,从免疫反应到像SARS-CoV-2这样的病毒感染.
现有的PPI数据库对于新研究或研究不足的物种往往不完整.
文本挖掘为构建PPI网络提供了有价值的替代方案.

研究的目的:

为了比较经典文本处理,基于机器学习 (ML) 的NLP和基于大型语言模型 (LLM) 的NLP工具的性能,以提取PPI关系.
评估通过不同的NLP方法生成的PPI网络的特征.

主要方法:

评估的开源经典文本处理工具.
评估基于ML的NLP方法用于关系提取.
对比基于LLM的NLP工具用于PPI网络构建.
分析网络属性,如真正率和网络结构.

主要成果:

古典方法产生了高的真实阳性率,但导致了过度连接的网络.
基于ML的NLP方法产生了较低的真实阳性率,但产生了与目标结构相似的网络.
基于LLM的NLP方法显示出不同的性能,通常在经典和ML方法之间.
模型性能受到提供的文本数据量的影响.

结论:

对于PPI网络构建的NLP方法的选择取决于研究的具体需求和数据的可用性.
当高灵敏度优先于网络特异性时,古典方法是合适的.
ML和LLM方法为更具结构代表性的网络提供了替代方案,性能根据数据量而异.

关键词:

贝尔特 (BERT) 公司在 GPT 中,GPT 必须是 GPT.法学士 (LLM) 是一个专业.生物文本采矿生物文本采矿大型语言模型.机器学习是机器学习.自然语言处理自然语言处理.关系提取关系提取

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