
PMC:7782580 / 1347-1829
Annnotations
LitCovid-PubTator
Id | Subject | Object | Predicate | Lexical cue | tao:has_database_id |
---|---|---|---|---|---|
19 | 13-41 | Disease | denotes | upper respiratory infections | MESH:D012141 |
20 | 191-199 | Disease | denotes | COVID-19 | MESH:C000657245 |
21 | 200-209 | Disease | denotes | infection | MESH:D007239 |
44 | 465-476 | Species | denotes | coronavirus | Tax:11118 |
49 | 246-270 | Disease | denotes | Coronavirus disease 2019 | MESH:C000657245 |
50 | 272-280 | Disease | denotes | COVID-19 | MESH:C000657245 |
51 | 292-310 | Disease | denotes | infectious disease | MESH:D003141 |
LitCovid-PD-HP
Id | Subject | Object | Predicate | Lexical cue | hp_id |
---|---|---|---|---|---|
T1 | 19-41 | Phenotype | denotes | respiratory infections | http://purl.obolibrary.org/obo/HP_0011947 |
LitCovid-sentences
Id | Subject | Object | Predicate | Lexical cue |
---|---|---|---|---|
T11 | 43-231 | 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 | 233-245 | Sentence | denotes | Introduction |