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通过类比来预测药物基因关系,使用词嵌入的任务

Hiroaki Yamagiwa¹, Ryoma Hashimoto², Kiwamu Arakane³

¹Kyoto University, Kyoto, Japan. h.yamagiwa@i.kyoto-u.ac.jp.

Scientific reports

|May 18, 2025

概括

生物概念Vec嵌入可以使用简单的矢量算法来预测药物基因关系. 这种方法,通过路径分类来增强,显示性能与大型语言模型相美.

科学领域:

计算生物学是一种计算生物学.
生物信息学是一种生物信息学.
自然语言处理自然语言处理.

背景情况:

词嵌入将文本转换为数值向量,通过向量算法实现类比任务.
生物概念Vec是一个在PubMed摘要上训练的生物嵌入模型.
药物-基因关系对于理解药理学和开发新疗法至关重要.

研究的目的:

调查BioConceptVec和类似的嵌入物是否捕获了药物基因关系.
评估这些嵌入的预测能力,以识别药物目标.
评估生物通路信息对预测性能的影响.

主要方法:

在PubMed摘要上训练有素的定制嵌入.
在嵌入物上利用类比计算 (矢量算术) 来预测药物-基因关系.
纳入药物和基因的生物通路分类.
根据年份划分的数据集的评估性能,以评估时间预测能力.

主要成果:

证明BioConceptVec和定制嵌入包含药物基因关系信息.
表明类比计算可以预测药物的向基因.
发现通过生物途径对实体进行分类可以显著提高预测性能.
证实,在过去的数据上训练的嵌入可以预测未来的药物基因关系.

结论:

矢量嵌入,如BioConceptVec,为预测药物基因相互作用提供了一个计算高效的方法.
整合生物通路信息可以提高这些预测的准确性.
这种以类比为基础的方法显示了作为药物基因关系发现复杂模型的可行替代方案的承诺.

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