来自1亿份生物医学文档的知识综合增强了冠状病毒受体的深度表达谱
Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors
摘要：
COVID-19大流行需要吸收所有可用的生物医学知识，以解码其致病性和传播机制。尽管最近复兴的无监督神经网络解码非结构化的自然语言，一个平台的实时合成的指数增长的生物医学文献及其全面的三角与深刻的组学见解是不可用的。在这里，我们提出了nferX平台，用于从非结构化生物医学文本中提取的超过45个四分之一可能的概念关联进行动态推理，以及它们与基于单细胞RNA测序的来自超过25种组织的见解的三角测量。利用这个平台，我们确定了COVID-19的病理表现与SARS-CoV-2受体ACE 2的综合表达谱之间的交叉点。我们发现，舌角化细胞和嗅觉上皮细胞可能是SARS-CoV-2感染的未被充分认识的靶点，与报告的味觉和嗅觉丧失相关，这是COVID-19感染的早期指标，包括在其他无症状的患者中。肺中的气道俱乐部细胞、纤毛细胞和II型肺细胞以及肠道的肠细胞也表达ACE 2。这项研究展示了一个整体的数据科学平台如何利用前所未有的结构化和非结构化公开数据来加速产生有影响力的生物学见解和假设。
英文摘要：
The COVID-19 pandemic demands assimilation of all available biomedical knowledge to decode its mechanisms of pathogenicity and transmission. Despite the recent renaissance in unsupervised neural networks for decoding unstructured natural languages, a platform for the real-time synthesis of the exponentially growing biomedical literature and its comprehensive triangulation with deep omic insights is not available. Here, we present the nferX platform for dynamic inference from over 45 quadrillion possible conceptual associations extracted from unstructured biomedical text, and their triangulation with Single Cell RNA-sequencing based insights from over 25 tissues. Using this platform, we identify intersections between the pathologic manifestations of COVID-19 and the comprehensive expression profile of the SARS-CoV-2 receptor ACE2. We find that tongue keratinocytes and olfactory epithelial cells are likely under-appreciated targets of SARS-CoV-2 infection, correlating with reported loss of sense of taste and smell as early indicators of COVID-19 infection, including in otherwise asymptomatic patients. Airway club cells, ciliated cells and type II pneumocytes in the lung, and enterocytes of the gut also express ACE2. This study demonstrates how a holistic data science platform can leverage unprecedented quantities of structured and unstructured publicly available data to accelerate the generation of impactful biological insights and hypotheses.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
