Sikirzhytskaya Aliaksandra, Tyagin Ilya, Sutton S Scott, Wyatt Michael D, Safro Ilya, Shtutman Michael
Department of Drug Discovery and Biomedical Sciences, College of Pharmacy, University of South Carolina, United States of America.
Department of Computer and Information Sciences, University of Delaware, United States of America.
Artif Intell Med. 2025 Oct;168:103218. doi: 10.1016/j.artmed.2025.103218. Epub 2025 Jul 10.
Neurodegenerative diseases like Alzheimer's, Parkinson's, and HIV-associated neurocognitive disorder severely impact patients and healthcare systems. While effective treatments remain limited, researchers are actively developing ways to slow progression and improve patient outcomes, requiring innovative approaches to handle huge volumes of new scientific data. To enable the automatic analysis of biomedical data we introduced AGATHA, an effective AI-based literature mining tool that can navigate massive scientific literature databases. The overarching goal of this effort is to adapt AGATHA for drug repurposing by revealing hidden connections between FDA-approved medications and a health condition of interest. Our tool converts the abstracts of peer-reviewed papers from PubMed into multidimensional space where each gene and health condition are represented by specific metrics. We implemented advanced statistical analysis to reveal distinct clusters of scientific terms within the virtual space created using AGATHA-calculated parameters for selected health conditions and genes. Partial Least Squares Discriminant Analysis was employed for categorizing and predicting samples (122 diseases and 20,889 genes) fitted to specific classes. Advanced statistics were employed to build a discrimination model and extract lists of genes specific to each disease class. We focused on repurposing drugs for dementia by identifying dementia-associated genes highly ranked in other disease classes. The method was developed for detection of genes that shared across multiple conditions and classified them based on their roles in biological pathways. This led to the selection of six primary drugs for further study.
像阿尔茨海默病、帕金森病和与人类免疫缺陷病毒相关的神经认知障碍等神经退行性疾病严重影响着患者和医疗保健系统。虽然有效的治疗方法仍然有限，但研究人员正在积极开发减缓疾病进展和改善患者预后的方法，这需要创新方法来处理大量新的科学数据。为了实现生物医学数据的自动分析，我们引入了AGATHA，这是一种基于人工智能的有效文献挖掘工具，它可以浏览海量的科学文献数据库。这项工作的总体目标是通过揭示美国食品药品监督管理局（FDA）批准的药物与感兴趣的健康状况之间的隐藏联系，使AGATHA适用于药物再利用。我们的工具将来自PubMed的同行评审论文摘要转换到多维空间，其中每个基因和健康状况都由特定指标表示。我们实施了先进的统计分析，以揭示在使用AGATHA计算的选定健康状况和基因参数创建的虚拟空间内不同的科学术语簇。采用偏最小二乘判别分析对适合特定类别的样本（122种疾病和20889个基因）进行分类和预测。采用先进的统计方法建立判别模型，并提取每个疾病类别的特定基因列表。我们通过识别在其他疾病类别中排名靠前的与痴呆相关的基因，专注于为痴呆症进行药物再利用。该方法是为检测在多种条件下共享的基因而开发的，并根据它们在生物途径中的作用对其进行分类。这导致了六种主要药物的选择以供进一步研究。
