通过文本挖掘构建高海拔疾病分子事件网络，识别关键生物标志物。
Unraveling the Biomarker Prospects of High-Altitude Diseases: Insights from Biomolecular Event Network Constructed using Text Mining
- 结合机器学习与图核方法从文献中提取150+分子事件，构建97节点网络。
- 基于中心性排序发现EPO、VEGF等蛋白是核心分子，参与缺氧响应与血管重塑。
- 适用于研究高海拔疾病机制的科研人员，尤其关注生物标志物发现者。
高海拔疾病（HAD）包括急性高山病（AMS）、高原脑水肿（HACE）和高原肺水肿（HAPE），由2500米以上低气压缺氧引发，健康风险显著，但分子机制仍不清晰。本研究整合监督学习与特征型及多尺度拉普拉斯图核方法，分析7,847篇经筛选的PubMed相关摘要，提取超过150种独特生物分子事件，包括基因表达、调控、结合与定位，并构建包含97个节点和153条边的加权无向分子事件网络。利用PageRank算法按中心性排序关键分子，前几位为促红细胞生成素（EPO，0.0163）、血管内皮生长因子（VEGF，0.0148）、缺氧诱导因子1α（HIF-1α，0.0136）、内皮 PAS 结构域蛋白1（EPAS1）、血管紧张素转换酶（ACE，0.0119）、EGLN1、内皮素1（ET-1）及70 kDa热休克蛋白（Hsp70，0.0118），均参与氧感知、血管重构、红细胞生成与血压调节。子网分析揭示以缺氧反应、炎症和应激适应为核心的三大功能模块。该整合方法展示了大规模文本挖掘与图分析在揭示机制及优先筛选潜在生物标志物方面的价值。
原文摘要 · Abstract (English)
High-altitude diseases (HAD), encompassing acute mountain sickness (AMS), high-altitude cerebral edema (HACE), and high-altitude pulmonary edema (HAPE), are triggered by hypobaric hypoxia at elevations above 2,500 meters. These conditions pose significant health risks, yet the molecular mechanisms remain insufficiently understood. In this study, we developed a biomolecular event extraction pipeline integrating supervised machine learning with feature-based and multiscale Laplacian graph kernels to analyze 7,847 curated HAD-related abstracts from PubMed. We extracted over 150 unique biomolecular events including gene expression, regulation, binding, and localization and constructed a weighted, undirected biomolecular event network comprising 97 nodes and 153 edges. Using the PageRank algorithm, we prioritized key biomolecules based on their centrality within the event network. The top-ranked proteins included Erythropoietin (EPO) (0.0163), Vascular endothelial growth factor (VEGF) (0.0148), Hypoxia-inducible factor 1 (HIF-1) alpha (0.0136), Endothelial PAS Domain Protein 1 (EPAS1) and Angiotensin-Converting Enzyme (ACE) (0.0119), Egl nine homolog 1 (EGLN1), Endothelin 1 (ET-1), and 70 kilodalton heat shock protein (Hsp70)(0.0118), all of which play crucial roles in oxygen sensing, vascular remodeling, erythropoiesis, and blood pressure regulation. Subnetwork analysis revealed three major functional clusters centered on hypoxia response, inflammation, and stress adaptation pathways. Our integrative approach demonstrates the utility of large-scale text mining and graph-based analysis to uncover mechanistic insights and prioritize potential biomarkers for high-altitude disease.
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