用AI从文献中挖掘微小RNA与药物关系，发现多种疾病潜在新药。
AI-Driven Drug Repurposing through miRNA-mRNA Relation
- 用微调的PubMedBERT模型从文献提取miRNA-mRNA关系
- 构建包含16417条边的多维度生物网络，发现595个miRNA-药物关联
- 首次系统整合miRNA、mRNA、药物和疾病四类实体的关系图谱
miRNA与mRNA的关系与多种生物过程及疾病机制密切相关。近期研究显示，大型语言模型在从PubMed中提取miRNA-mRNA关系方面表现良好，其中PubMedBERT在miRNA-mRNA相互作用语料库（MMIC）上取得了0.783的F1分数。本文首次将微调后的PubMedBERT模型应用于慢性阻塞性肺病（COPD）、阿尔茨海默病（AD）、中风、2型糖尿病（T2DM）、慢性肝病及癌症等疾病的PubMed文献，提取miRNA-mRNA关系；随后利用KinderMiner工具检索miRNA-药物关系；进而构建三个交互网络：疾病中心网络、药物中心网络和miRNA中心网络，共包含3497个节点和16417条有向边。最后通过MIMIC IV数据集验证候选药物。该综合方法揭示了研究疾病中的已知与新候选药物，共提取595个miRNA-药物关系。据我们所知，这是首个系统性提取并可视化miRNA、mRNA、药物与疾病之间关系的研究。
原文摘要 · Abstract (English)
miRNA mRNA relations are closely linked to several biological processes and disease mechanisms In a recent study we tested the performance of large language models LLMs on extracting miRNA mRNA relations from PubMed PubMedBERT achieved the best performance of 0.783 F1 score for miRNA mRNA Interaction Corpus MMIC Here we first applied the finetuned PubMedBERT model to extract miRNA mRNA relations from PubMed for chronic obstructive pulmonary disease COPD Alzheimers disease AD stroke type 2 diabetes mellitus T2DM chronic liver disease and cancer Next we retrieved miRNA drug relations using KinderMiner a literature mining tool for relation extraction Then we constructed three interaction networks 1 disease centric network 2 drug centric network and 3 miRNA centric network comprising 3497 nodes and 16417 edges organized as a directed graph to capture complex biological relationships Finally we validated the drugs using MIMIC IV Our integrative approach revealed both established and novel candidate drugs for diseases under study through 595 miRNA drug relations extracted from PubMed To the best of our knowledge this is the first study to systematically extract and visualize relationships among four distinct biomedical entities miRNA mRNA drug and disease
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