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

Tyler Wagner, Samir Awasthi, Gayle Wittenberg, AJ Venkatakrishnan, Dan Tarjan, Anuli Anyanwu-Ofili, Andrew Badley, John Halamka, Christopher Flores, Najat Khan, Rakesh Barve, View ORCID ProfileVenky Soundararajan
doi: https://doi.org/10.1101/2020.04.03.020602
Tyler Wagner
1nference, inc., One Main St, East Arcade, Suite 400, Cambridge, MA 02142, USA
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Samir Awasthi
1nference, inc., One Main St, East Arcade, Suite 400, Cambridge, MA 02142, USA
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Gayle Wittenberg
2Janssen pharmaceutical companies of Johnson & Johnson (J&J), USA
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AJ Venkatakrishnan
1nference, inc., One Main St, East Arcade, Suite 400, Cambridge, MA 02142, USA
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Dan Tarjan
1nference, inc., One Main St, East Arcade, Suite 400, Cambridge, MA 02142, USA
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Anuli Anyanwu-Ofili
2Janssen pharmaceutical companies of Johnson & Johnson (J&J), USA
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Andrew Badley
3Mayo Clinic, Rochester, MN 55905, USA
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John Halamka
3Mayo Clinic, Rochester, MN 55905, USA
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Christopher Flores
2Janssen pharmaceutical companies of Johnson & Johnson (J&J), USA
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Najat Khan
2Janssen pharmaceutical companies of Johnson & Johnson (J&J), USA
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Rakesh Barve
1nference, inc., One Main St, East Arcade, Suite 400, Cambridge, MA 02142, USA
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Venky Soundararajan
1nference, inc., One Main St, East Arcade, Suite 400, Cambridge, MA 02142, USA
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  • ORCID record for Venky Soundararajan
  • For correspondence: venky@nference.net
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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/).

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted April 04, 2020.
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Real-time biomedical knowledge synthesis of the exponentially growing world wide web using unsupervised neural networks
Tyler Wagner, Samir Awasthi, Gayle Wittenberg, AJ Venkatakrishnan, Dan Tarjan, Anuli Anyanwu-Ofili, Andrew Badley, John Halamka, Christopher Flores, Najat Khan, Rakesh Barve, Venky Soundararajan
bioRxiv 2020.04.03.020602; doi: https://doi.org/10.1101/2020.04.03.020602
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Real-time biomedical knowledge synthesis of the exponentially growing world wide web using unsupervised neural networks
Tyler Wagner, Samir Awasthi, Gayle Wittenberg, AJ Venkatakrishnan, Dan Tarjan, Anuli Anyanwu-Ofili, Andrew Badley, John Halamka, Christopher Flores, Najat Khan, Rakesh Barve, Venky Soundararajan
bioRxiv 2020.04.03.020602; doi: https://doi.org/10.1101/2020.04.03.020602

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