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NCRDLLM:通过多模式特征融合和大型语言模型预测ncRNA-药物反应关联

Zihan Zhang¹, Yuchen Zhang¹

¹College of Information Engineering, Northwest A & F University, Yangling, Shaanxi 712100, China.

Journal of chemical information and modeling

|March 12, 2026

概括

本研究介绍了NCRDLLM,这是一种使用大型语言模型 (LLM) 预测非编码RNA (ncRNA) 与药物之间的关联的新框架. 它通过整合各种ncRNA类型和多式特征来增强癌症药物反应预测.

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:

非编码RNAs (ncRNAs) 是癌症药物反应的关键调节者.
现有的方法难以处理多式联络数据和有限的ncRNA类型,这影响了概括性.
需要采用统一的方法来捕捉复杂的ncRNA-药物相互作用.

研究的目的:

开发NCRDLLM,一个统一的框架,利用大型语言模型 (LLM) 来预测三种ncRNA类型 (circRNA,miRNA,lncRNA) 和药物之间的关联.
克服现有方法在处理多模式生物特征方面的局限性,提高预测稳定性.
为了确定增强癌症治疗的潜在ncRNA-药物反应关联.

主要方法:

整合了19020个经过验证的ncRNA药物关联和120009个疾病关联记录.
构建的多模式特征:序列 (RNA-FM,ChemBERTa),结构 (Graph2Vec,AttentiveFP,ECFP) 和关联 (基于疾病).
使用LLaMA-3.2-3B LLM与适配器模块和LoRA进行参数高效微调.

主要成果:

获得了高AUC-ROC值:0.9665 (miRNA药物),0.9832 (lncRNA药物) 和0.9676 (circRNA药物).
废弃性研究验证了每个框架模块的贡献.
文献证据和表达概况支持预测的ncRNA药物关联的生物学相关性.

结论:

NCRDLLM提供了一种有效的统一策略,用于预测ncRNA与药物反应的关联.
该框架通过整合多式联运特征,表现出强大的通用性和稳定性.
这种方法有望通过新型ncRNA药物点识别来推进个性化癌症医学.

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