About: Real-time biomedical knowledge synthesis of the exponentially growing world wide web using unsupervised neural networks

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About: Real-time biomedical knowledge synthesis of the exponentially growing world wide web using unsupervised neural networks Goto Sponge NotDistinct Permalink

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

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Real-time biomedical knowledge synthesis of the exponentially growing world wide web using unsupervised neural networks
Creator	Anyanwu-Ofili, Anuli Barve, Rakesh Khan, Najat Soundararajan, Venky Venkatakrishnan, A Wagner, Tyler Awasthi, Samir Badley, Andrew Flores, Christopher Halamka, John Tarjan, Dan Wittenberg, Gayle
topic	covid:8b4234b45ff6bcac91aeec02379a651f6a93ccfa#this
Source	BioRxiv
abstract	Decoding disease mechanisms for addressing unmet clinical need demands the rapid assimilation of the exponentially growing biomedical knowledge. These are either inherently unstructured and non-conducive to current computing paradigms or siloed into structured databases requiring specialized bioinformatics. Despite the recent renaissance in unsupervised neural networks for deciphering unstructured natural languages and the availability of numerous bioinformatics resources, a holistic platform for real-time synthesis of the scientific literature and seamless triangulation with deep omic insights and real-world evidence has not been advanced. Here, we introduce the nferX platform that makes the highly unstructured biomedical knowledge computable and supports the seamless visual triangulation with statistical inference from diverse structured databases. The nferX platform will accelerate and amplify the research potential of subject-matter experts as well as non-experts across the life science ecosystem (https://academia.nferx.com/).
has issue date	2020-04-04(xsd:dateTime)
bibo:doi	10.1101/2020.04.03.020602
has license	biorxiv
sha1sum (hex)	8b4234b45ff6bcac91aeec02379a651f6a93ccfa
schema:url	https://doi.org/10.1101/2020.04.03.020602
resource representing a document's title	Real-time biomedical knowledge synthesis of the exponentially growing world wide web using unsupervised neural networks
schema:publication	bioRxiv
resource representing a document's body	covid:8b4234b45ff6bcac91aeec02379a651f6a93ccfa#body_text
is http://vocab.deri.ie/void#inDataset of	proxy:http/ns.inria.fr/covid19/8b4234b45ff6bcac91aeec02379a651f6a93ccfa
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