Department of Clinical and Experimental Medicine, University of Catania, Catania, 95125, Italy.
Scuola Normale Superiore, Pisa, 56126 , Italy.
Bioinformatics. 2024 May 2;40(5). doi: 10.1093/bioinformatics/btae194.
The rapid increase of bio-medical literature makes it harder and harder for scientists to keep pace with the discoveries on which they build their studies. Therefore, computational tools have become more widespread, among which network analysis plays a crucial role in several life-science contexts. Nevertheless, building correct and complete networks about some user-defined biomedical topics on top of the available literature is still challenging.
We introduce NetMe 2.0, a web-based platform that automatically extracts relevant biomedical entities and their relations from a set of input texts-i.e. in the form of full-text or abstract of PubMed Central's papers, free texts, or PDFs uploaded by users-and models them as a BioMedical Knowledge Graph (BKG). NetMe 2.0 also implements an innovative Retrieval Augmented Generation module (Graph-RAG) that works on top of the relationships modeled by the BKG and allows the distilling of well-formed sentences that explain their content. The experimental results show that NetMe 2.0 can infer comprehensive and reliable biological networks with significant Precision-Recall metrics when compared to state-of-the-art approaches.
生物医学文献的快速增长使得科学家们越来越难以跟上他们研究的发现。因此，计算工具变得更加普及，其中网络分析在几个生命科学领域中起着至关重要的作用。然而，在现有的文献基础上，为一些用户定义的生物医学主题构建正确和完整的网络仍然具有挑战性。
我们引入了 NetMe 2.0，这是一个基于网络的平台，它可以自动从一组输入文本（即 PubMed Central 论文的全文或摘要、用户上传的自由文本或 PDF 形式）中提取相关的生物医学实体及其关系，并将其建模为生物医学知识图（BKG）。NetMe 2.0 还实现了一个创新的检索增强生成模块（Graph-RAG），该模块基于 BKG 建模的关系运行，并允许提取解释其内容的结构良好的句子。实验结果表明，与最先进的方法相比，NetMe 2.0 可以推断出具有显著精度-召回指标的全面和可靠的生物网络。
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