About: Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors

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About: Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors
Creator	Wu, Xiaoying Anand, Akash Anyanwu-Ofili, Anuli Barve, Rakesh Chilaka, Ramakrishna »more»
source	BioRxiv ArXiv
abstract	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, airway club cells, and ciliated cells are likely underappreciated targets of SARS-CoV-2 infection, in addition to type II pneumocytes and olfactory epithelial cells. We further identify mature small intestinal enterocytes as a possible hotspot of COVID-19 fecal-oral transmission, where an intriguing maturation-correlated transcriptional signature is shared between ACE2 and the other coronavirus receptors DPP4 (MERS-CoV) and ANPEP (α-coronavirus). 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. The nferX Platform Single-cell resource - https://academia.nferx.com/
has issue date	2020-03-28(xsd:dateTime) 2020-03-29(xsd:dateTime)
bibo:doi	10.1101/2020.03.24.005702
has license	biorxiv arxiv
sha1sum (hex)	d1dde1df11f93e8eae0d0b467cd0455afdc5b98c
schema:url	https://doi.org/10.1101/2020.03.24.005702
resource representing a document's title	Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors
schema:publication	bioRxiv
resource representing a document's body	covid:d1dde1df11f93e8eae0d0b467cd0455afdc5b98c#body_text
is schema:about of	named entity 'biological' named entity 'club cells' named entity 'accelerate' named entity 'hypotheses' named entity 'exponentially growing' »more»

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