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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