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.
Search related documents:
Co phrase search for related documents- expression similarity and gene expression: 1, 2, 3, 4, 5, 6, 7, 8, 9
- gene expression and individual cell: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22
- gene expression and query gene: 1, 2, 3, 4, 5
- gene expression and run time: 1, 2
- gene query gene and query gene: 1
Co phrase search for related documents, hyperlinks ordered by date