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

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

While unsupervised machine learning has been advanced to study the semantic relationships between word embeddings 14, 15 and applied to the material science corpus 16 , this has not been scaled-up to extract the "global context" of conceptual associations from the entirety of publicly available unstructured biomedical text. Additionally, a principled way of accounting for the distances between phrases captured from the ever-growing scientific literature has not been comprehensively researched to quantify the strength of "local context" between pairs of biological concepts. Given the propensity for irreproducible or erroneous scientific research 17 , which reflects as truths, semi-truths, and falsities in the literature, any local or global signals extracted from this unstructured knowledge need to be seamlessly triangulated with deep biological insights emergent from various omics data silos. author/funder. All rights reserved. No reuse allowed without permission.