PMC:7402624 / 74191-75186
Annnotations
LitCovid-PubTator
| Id | Subject | Object | Predicate | Lexical cue | tao:has_database_id |
|---|---|---|---|---|---|
| 2169 | 67-75 | Species | denotes | patients | Tax:9606 |
| 2170 | 333-340 | Species | denotes | patient | Tax:9606 |
| 2171 | 669-673 | Chemical | denotes | UMAP | |
| 2172 | 58-66 | Disease | denotes | COVID-19 | MESH:C000657245 |
| 2173 | 324-332 | Disease | denotes | COVID-19 | MESH:C000657245 |
LitCovid-PD-MONDO
| Id | Subject | Object | Predicate | Lexical cue | mondo_id |
|---|---|---|---|---|---|
| T438 | 58-66 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T439 | 324-332 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
LitCovid-PD-CLO
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T1053 | 0-1 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | A |
| T1054 | 98-99 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
| T1055 | 135-136 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | A |
| T1056 | 744-745 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | A |
| T1057 | 832-833 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
LitCovid-PD-CHEBI
| Id | Subject | Object | Predicate | Lexical cue | chebi_id |
|---|---|---|---|---|---|
| T50300 | 131-133 | Chemical | denotes | S8 | http://purl.obolibrary.org/obo/CHEBI_29385 |
| T64153 | 202-206 | Chemical | denotes | base | http://purl.obolibrary.org/obo/CHEBI_22695 |
| T67801 | 275-279 | Chemical | denotes | base | http://purl.obolibrary.org/obo/CHEBI_22695 |
| T2551 | 806-810 | Chemical | denotes | base | http://purl.obolibrary.org/obo/CHEBI_22695 |
LitCovid-sentences
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T461 | 0-143 | Sentence | denotes | A feature-weighted kernel density was computed across all COVID-19 patients, and was displayed as a contour plot (Fig. 6G and fig. S8, A to D). |
| T462 | 144-530 | Sentence | denotes | Whereas traditional kernel density methods apply the same base kernel function to every point to visualize point density, here the base kernel function centered at each individual COVID-19 patient sample was instead weighted (multiplied) by the Z-transform (mean-centered and standard deviation-scaled) of the log-transformed input feature prior to computing the overall kernel density. |
| T463 | 531-743 | Sentence | denotes | This weighting procedure facilitated visualization of the overall feature gradients (going from relatively low-to-high expression) across UMAP coordinates independent of the different range of each input feature. |
| T464 | 744-995 | Sentence | denotes | A radially symmetric two-dimensional Gaussian was used as the base kernel function with a variance parameter equal to one-half, which was tuned to be sufficiently broad in order to smooth out local discontinuities and best visualize feature gradients. |