PubMed:33169099
Annnotations
LitCovid-PD-FMA-UBERON
Id | Subject | Object | Predicate | Lexical cue | fma_id |
---|---|---|---|---|---|
T1 | 588-592 | Body_part | denotes | lung | http://purl.org/sig/ont/fma/fma7195 |
T2 | 640-647 | Body_part | denotes | thyroid | http://purl.org/sig/ont/fma/fma9603 |
LitCovid-PD-UBERON
Id | Subject | Object | Predicate | Lexical cue | uberon_id |
---|---|---|---|---|---|
T1 | 588-592 | Body_part | denotes | lung | http://purl.obolibrary.org/obo/UBERON_0002048 |
T2 | 640-647 | Body_part | denotes | thyroid | http://purl.obolibrary.org/obo/UBERON_0002046 |
LitCovid-PD-MONDO
Id | Subject | Object | Predicate | Lexical cue | mondo_id |
---|---|---|---|---|---|
T1 | 60-68 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
T2 | 136-144 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
T3 | 257-265 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
T4 | 556-576 | Disease | denotes | diabetic retinopathy | http://purl.obolibrary.org/obo/MONDO_0005266 |
T5 | 565-576 | Disease | denotes | retinopathy | http://purl.obolibrary.org/obo/MONDO_0005283 |
T6 | 772-780 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
T7 | 1036-1044 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
T8 | 1286-1294 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
T9 | 1416-1424 | Disease | denotes | COVID-19 | http://purl.obolibrary.org/obo/MONDO_0100096 |
LitCovid-PD-CLO
Id | Subject | Object | Predicate | Lexical cue |
---|---|---|---|---|
T1 | 80-81 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | A |
T2 | 155-158 | http://purl.obolibrary.org/obo/CLO_0051582 | denotes | has |
T3 | 211-216 | http://purl.obolibrary.org/obo/NCBITaxon_9606 | denotes | human |
T4 | 299-311 | http://purl.obolibrary.org/obo/OBI_0000245 | denotes | organization |
T5 | 340-343 | http://purl.obolibrary.org/obo/CLO_0051582 | denotes | has |
T6 | 352-353 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
T7 | 472-477 | http://purl.obolibrary.org/obo/UBERON_0007688 | denotes | field |
T8 | 507-510 | http://purl.obolibrary.org/obo/CLO_0051582 | denotes | has |
T9 | 588-592 | http://purl.obolibrary.org/obo/UBERON_0002048 | denotes | lung |
T10 | 588-592 | http://www.ebi.ac.uk/efo/EFO_0000934 | denotes | lung |
T11 | 740-741 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
T12 | 815-816 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
T13 | 937-942 | http://purl.obolibrary.org/obo/CLO_0009985 | denotes | focus |
LitCovid-PD-GO-BP
Id | Subject | Object | Predicate | Lexical cue |
---|---|---|---|---|
T1 | 5-13 | http://purl.obolibrary.org/obo/GO_0007612 | denotes | Learning |
T2 | 498-506 | http://purl.obolibrary.org/obo/GO_0007612 | denotes | learning |
T3 | 622-634 | http://purl.obolibrary.org/obo/GO_0051179 | denotes | localization |
T4 | 731-739 | http://purl.obolibrary.org/obo/GO_0007612 | denotes | learning |
T5 | 1010-1018 | http://purl.obolibrary.org/obo/GO_0007612 | denotes | learning |
T6 | 1108-1116 | http://purl.obolibrary.org/obo/GO_0007612 | denotes | learning |
T7 | 1260-1268 | http://purl.obolibrary.org/obo/GO_0007612 | denotes | learning |
T8 | 1387-1395 | http://purl.obolibrary.org/obo/GO_0007612 | denotes | learning |
LitCovid-PubTator
Id | Subject | Object | Predicate | Lexical cue | tao:has_database_id |
---|---|---|---|---|---|
2 | 47-58 | Species | denotes | Coronavirus | Tax:11118 |
3 | 60-68 | Disease | denotes | COVID-19 | MESH:C000657245 |
14 | 115-134 | Disease | denotes | coronavirus disease | MESH:D018352 |
15 | 136-144 | Disease | denotes | COVID-19 | MESH:C000657245 |
16 | 171-176 | Disease | denotes | death | MESH:D003643 |
17 | 211-216 | Species | denotes | human | Tax:9606 |
18 | 257-265 | Disease | denotes | COVID-19 | MESH:C000657245 |
19 | 556-576 | Disease | denotes | diabetic retinopathy | MESH:D003920 |
20 | 772-780 | Disease | denotes | COVID-19 | MESH:C000657245 |
21 | 1036-1044 | Disease | denotes | COVID-19 | MESH:C000657245 |
22 | 1286-1294 | Disease | denotes | COVID-19 | MESH:C000657245 |
23 | 1416-1424 | Disease | denotes | COVID-19 | MESH:C000657245 |
LitCovid-sentences
Id | Subject | Object | Predicate | Lexical cue |
---|---|---|---|---|
T1 | 0-79 | Sentence | denotes | Deep Learning and Medical Image Processing for Coronavirus (COVID-19) Pandemic: |
T2 | 80-89 | Sentence | denotes | A Survey. |
T3 | 90-222 | Sentence | denotes | Since December 2019, the coronavirus disease (COVID-19) outbreak has caused many death cases and affected all sectors of human life. |
T4 | 223-464 | Sentence | denotes | With gradual progression of time, COVID-19 was declared by the world health organization (WHO) as an outbreak, which has imposed a heavy burden on almost all countries, especially ones with weaker health systems and ones with slow responses. |
T5 | 465-658 | Sentence | denotes | In the field of healthcare, deep learning has been implemented in many applications, e.g., diabetic retinopathy detection, lung nodule classification, fetal localization, and thyroid diagnosis. |
T6 | 659-790 | Sentence | denotes | Numerous sources of medical images (e.g., X-ray, CT, and MRI) make deep learning a great technique to combat the COVID-19 outbreak. |
T7 | 791-912 | Sentence | denotes | Motivated by this fact, a large number of research works have been proposed and developed for the initial months of 2020. |
T8 | 913-1070 | Sentence | denotes | In this paper, we first focus on summarizing the state-of-the-art research works related to deep learning applications for COVID-19 medical image processing. |
T9 | 1071-1177 | Sentence | denotes | Then, we provide an overview of deep learning and its applications to healthcare found in the last decade. |
T10 | 1178-1320 | Sentence | denotes | Next, three use cases in China, Korea, and Canada are also presented to show deep learning applications for COVID-19 medical image processing. |
T11 | 1321-1589 | Sentence | denotes | Finally, we discuss several challenges and issues related to deep learning implementations for COVID-19 medical image processing, which are expected to drive further studies in controlling the outbreak and controlling the crisis, which results in smart healthy cities. |
LitCovid-PD-HP
Id | Subject | Object | Predicate | Lexical cue | hp_id |
---|---|---|---|---|---|
T1 | 565-576 | Phenotype | denotes | retinopathy | http://purl.obolibrary.org/obo/HP_0000488 |
LitCovid_AGAC_only
Id | Subject | Object | Predicate | Lexical cue |
---|---|---|---|---|
p286140s14 | 159-165 | Reg | denotes | caused |