生物
计算生物学
冠状病毒
仿形(计算机编程)
受体
2019年冠状病毒病(COVID-19)
细胞生物学
生物信息学
传染病(医学专业)
计算机科学
遗传学
医学
病理
操作系统
疾病
作者
AJ Venkatakrishnan,Arjun Puranik,Akash Anand,David Zemmour,Xiang Yao,Xiaoying Wu,Ramakrishna Chilaka,Dariusz K. Murakowski,Kristopher Standish,Bharathwaj Raghunathan,Tyler Wagner,Enrique Garcia-Rivera,Hugo Solomon,Abhinav Garg,Rakesh Barve,Anuli Anyanwu-Ofili,Najat Khan,Venky Soundararajan
出处
期刊:eLife
[eLife Sciences Publications, Ltd.]
日期:2020-05-28
卷期号:9
被引量:49
摘要
The COVID-19 pandemic demands assimilation of all biomedical knowledge to decode mechanisms of pathogenesis. Despite the recent renaissance in neural networks, a platform for the real-time synthesis of the exponentially growing biomedical literature and deep omics insights is unavailable. Here, we present the nferX platform for dynamic inference from over 45 quadrillion possible conceptual associations from unstructured text, and triangulation with insights from single-cell RNA-sequencing, bulk RNA-seq and proteomics from diverse tissue types. A hypothesis-free profiling of ACE2 suggests tongue keratinocytes, olfactory epithelial cells, airway club cells and respiratory ciliated cells as potential reservoirs of the SARS-CoV-2 receptor. We find the gut as the putative hotspot of COVID-19, where a maturation correlated transcriptional signature is shared in small intestine enterocytes among coronavirus receptors (ACE2, DPP4, ANPEP). A holistic data science platform triangulating insights from structured and unstructured data holds potential for accelerating the generation of impactful biological insights and hypotheses.
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