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

March 26,2021

 
April 4, 2020
 

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/).

 
Authors:
Tyler Wagner1Samir Awasthi1Gayle Wittenberg2, AJ Venkatakrishnan1Dan Tarjan1Anuli Anyanwu-Ofili2, Andrew Badley3John Halamka3Christopher Flores2, Najat Khan2, Rakesh Barve1, Venky Soundararajan1
 
1nference, Cambridge, MA 02142
2Janssen pharmaceutical companies of Johnson & Johnson (J&J)
3Mayo Clinic, Rochester, MN 55905
 
Correspondence: Venky Soundararajan (venky@nference.net)
 
Affiliations:
 
 
Copyright:
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.