PMC:7782580 / 1320-1640
Annnotations
LitCovid-PubTator
| Id | Subject | Object | Predicate | Lexical cue | tao:has_database_id |
|---|---|---|---|---|---|
| 19 | 40-68 | Disease | denotes | upper respiratory infections | MESH:D012141 |
| 20 | 218-226 | Disease | denotes | COVID-19 | MESH:C000657245 |
| 21 | 227-236 | Disease | denotes | infection | MESH:D007239 |
| 49 | 273-297 | Disease | denotes | Coronavirus disease 2019 | MESH:C000657245 |
| 50 | 299-307 | Disease | denotes | COVID-19 | MESH:C000657245 |
LitCovid-PD-HP
| Id | Subject | Object | Predicate | Lexical cue | hp_id |
|---|---|---|---|---|---|
| T1 | 46-68 | Phenotype | denotes | respiratory infections | http://purl.obolibrary.org/obo/HP_0011947 |
LitCovid-sentences
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T11 | 70-258 | Sentence | denotes | Compared to expert evaluation of the images, the neural network achieved upwards of 99% specificity, showing promise for the automated detection of COVID-19 infection in clinical settings. |
| T12 | 260-272 | Sentence | denotes | Introduction |