PubMed:33161334
Annnotations
LitCovid-PD-FMA-UBERON
| Id | Subject | Object | Predicate | Lexical cue | fma_id |
|---|---|---|---|---|---|
| T1 | 294-309 | Body_part | denotes | neural networks | http://purl.org/sig/ont/fma/fma74616 |
| T2 | 392-397 | Body_part | denotes | lungs | http://purl.org/sig/ont/fma/fma68877 |
| T3 | 676-680 | Body_part | denotes | lung | http://purl.org/sig/ont/fma/fma7195 |
| T4 | 969-974 | Body_part | denotes | lungs | http://purl.org/sig/ont/fma/fma68877 |
| T5 | 1084-1089 | Body_part | denotes | chest | http://purl.org/sig/ont/fma/fma9576 |
| T6 | 1201-1216 | Body_part | denotes | neural networks | http://purl.org/sig/ont/fma/fma74616 |
LitCovid-PD-UBERON
| Id | Subject | Object | Predicate | Lexical cue | uberon_id |
|---|---|---|---|---|---|
| T1 | 676-680 | Body_part | denotes | lung | http://purl.obolibrary.org/obo/UBERON_0002048 |
| T2 | 1084-1089 | Body_part | denotes | chest | http://purl.obolibrary.org/obo/UBERON_0001443 |
LitCovid-PD-MONDO
| Id | Subject | Object | Predicate | Lexical cue | mondo_id |
|---|---|---|---|---|---|
| T1 | 36-44 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T2 | 187-195 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T3 | 452-460 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T4 | 493-501 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T5 | 578-586 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T6 | 667-675 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T7 | 770-778 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T8 | 869-877 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T9 | 1020-1028 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
| T10 | 1057-1066 | Disease | denotes | pneumonia | http://purl.obolibrary.org/obo/MONDO_0005249 |
| T11 | 1221-1229 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
LitCovid-PD-CLO
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T1 | 392-397 | http://www.ebi.ac.uk/efo/EFO_0000934 | denotes | lungs |
| T2 | 550-551 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
| T3 | 676-680 | http://purl.obolibrary.org/obo/UBERON_0002048 | denotes | lung |
| T4 | 676-680 | http://www.ebi.ac.uk/efo/EFO_0000934 | denotes | lung |
| T5 | 739-740 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
| T6 | 831-832 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
| T7 | 862-865 | http://purl.obolibrary.org/obo/PR_000001343 | denotes | aim |
| T8 | 924-925 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
| T9 | 969-974 | http://www.ebi.ac.uk/efo/EFO_0000934 | denotes | lungs |
| T10 | 1084-1089 | http://www.ebi.ac.uk/efo/EFO_0000965 | denotes | chest |
LitCovid-PD-HP
| Id | Subject | Object | Predicate | Lexical cue | hp_id |
|---|---|---|---|---|---|
| T1 | 1057-1066 | Phenotype | denotes | pneumonia | http://purl.obolibrary.org/obo/HP_0002090 |
LitCovid-PD-GO-BP
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T1 | 87-95 | http://purl.obolibrary.org/obo/GO_0007612 | denotes | learning |
| T2 | 232-240 | http://purl.obolibrary.org/obo/GO_0007612 | denotes | learning |
LitCovid-PubTator
| Id | Subject | Object | Predicate | Lexical cue | tao:has_database_id |
|---|---|---|---|---|---|
| 1 | 36-44 | Disease | denotes | COVID-19 | MESH:C000657245 |
| 14 | 187-195 | Disease | denotes | COVID-19 | MESH:C000657245 |
| 15 | 382-390 | Species | denotes | patients | Tax:9606 |
| 16 | 452-460 | Disease | denotes | COVID-19 | MESH:C000657245 |
| 17 | 480-485 | Disease | denotes | COVID | MESH:C000657245 |
| 18 | 493-501 | Disease | denotes | COVID-19 | MESH:C000657245 |
| 19 | 578-586 | Disease | denotes | COVID-19 | MESH:C000657245 |
| 20 | 667-675 | Disease | denotes | COVID-19 | MESH:C000657245 |
| 21 | 770-778 | Disease | denotes | COVID-19 | MESH:C000657245 |
| 22 | 869-877 | Disease | denotes | COVID-19 | MESH:C000657245 |
| 23 | 1020-1028 | Disease | denotes | COVID-19 | MESH:C000657245 |
| 24 | 1057-1066 | Disease | denotes | pneumonia | MESH:D011014 |
| 25 | 1221-1229 | Disease | denotes | COVID-19 | MESH:C000657245 |
LitCovid-sentences
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T1 | 0-104 | Sentence | denotes | The importance of standardisation - COVID-19 CT & Radiograph Image Data Stock for deep learning purpose. |
| T2 | 105-222 | Sentence | denotes | With the number of affected individuals still growing world-wide, the research on COVID-19 is continuously expanding. |
| T3 | 223-398 | Sentence | denotes | The deep learning community concentrates their efforts on exploring if neural networks can potentially support the diagnosis using CT and radiograph images of patients' lungs. |
| T4 | 399-524 | Sentence | denotes | The two most popular publicly available datasets for COVID-19 classification are COVID-CT and COVID-19 Image Data Collection. |
| T5 | 525-620 | Sentence | denotes | In this work, we propose a new dataset which we call COVID-19 CT & Radiograph Image Data Stock. |
| T6 | 621-796 | Sentence | denotes | It contains both CT and radiograph samples of COVID-19 lung findings and combines them with additional data to ensure a sufficient number of diverse COVID-19-negative samples. |
| T7 | 797-857 | Sentence | denotes | Moreover, it is supplemented with a carefully defined split. |
| T8 | 858-1090 | Sentence | denotes | The aim of COVID-19 CT & Radiograph Image Data Stock is to create a public pool of CT and radiograph images of lungs to increase the efficiency of distinguishing COVID-19 disease from other types of pneumonia and from healthy chest. |
| T9 | 1091-1294 | Sentence | denotes | We hope that the creation of this dataset would allow standardisation of the approach taken for training deep neural networks for COVID-19 classification and eventually for building more reliable models. |