Author: 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
Title: Knowledge synthesis from 100 million biomedical documents augments the deep expression profiling of coronavirus receptors Document date: 2020_3_29
ID: j7t9nebs_69
Snippet: Within the platform, we support the run-time computation of cosine similarity (i.e. 1 -cosine distance) between the queried gene and all other genes. This provides a measure of expression similarity across cells and can be used to identify co-regulated and co-expressed genes. Specifically, to perform this computation, we construct a "gene expression vector" for each gene G. This corresponds to the set of CP10K values for gene G in each individual.....
Document: Within the platform, we support the run-time computation of cosine similarity (i.e. 1 -cosine distance) between the queried gene and all other genes. This provides a measure of expression similarity across cells and can be used to identify co-regulated and co-expressed genes. Specifically, to perform this computation, we construct a "gene expression vector" for each gene G. This corresponds to the set of CP10K values for gene G in each individual cell from the selected populations in the selected study.
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