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LitCovid-PD-FMA-UBERON

Id Subject Object Predicate Lexical cue fma_id
T1 41-55 Body_part denotes neural network http://purl.org/sig/ont/fma/fma74616
T2 100-105 Body_part denotes chest http://purl.org/sig/ont/fma/fma9576
T3 429-434 Body_part denotes chest http://purl.org/sig/ont/fma/fma9576
T4 517-522 Body_part denotes chest http://purl.org/sig/ont/fma/fma9576
T5 743-757 Body_part denotes neural network http://purl.org/sig/ont/fma/fma74616
T6 815-820 Body_part denotes chest http://purl.org/sig/ont/fma/fma9576

LitCovid-PD-UBERON

Id Subject Object Predicate Lexical cue uberon_id
T1 100-105 Body_part denotes chest http://purl.obolibrary.org/obo/UBERON_0001443
T2 429-434 Body_part denotes chest http://purl.obolibrary.org/obo/UBERON_0001443
T3 517-522 Body_part denotes chest http://purl.obolibrary.org/obo/UBERON_0001443
T4 815-820 Body_part denotes chest http://purl.obolibrary.org/obo/UBERON_0001443

LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T1 80-88 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T2 124-148 Disease denotes Coronavirus Disease 2019 http://purl.obolibrary.org/obo/MONDO_0100096
T3 150-158 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T4 300-308 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T5 589-597 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T6 795-803 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T7 998-1006 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T8 1257-1265 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T9 2061-2069 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T1 11-12 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T2 100-105 http://www.ebi.ac.uk/efo/EFO_0000965 denotes chest
T3 187-188 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T4 263-264 http://purl.obolibrary.org/obo/CLO_0001020 denotes A
T5 429-434 http://www.ebi.ac.uk/efo/EFO_0000965 denotes chest
T6 517-522 http://www.ebi.ac.uk/efo/EFO_0000965 denotes chest
T7 722-723 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T8 815-820 http://www.ebi.ac.uk/efo/EFO_0000965 denotes chest
T9 1585-1586 http://purl.obolibrary.org/obo/CLO_0001020 denotes a
T10 1725-1726 http://purl.obolibrary.org/obo/CLO_0001020 denotes a

LitCovid-PD-CHEBI

Id Subject Object Predicate Lexical cue chebi_id
T1 1744-1752 Chemical denotes solution http://purl.obolibrary.org/obo/CHEBI_75958

LitCovid-PD-GO-BP

Id Subject Object Predicate Lexical cue
T1 2028-2036 http://purl.obolibrary.org/obo/GO_0007612 denotes learning

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
2 0-5 Disease denotes COVID MESH:C000657245
3 80-88 Disease denotes COVID-19 MESH:C000657245
24 124-148 Disease denotes Coronavirus Disease 2019 MESH:C000657245
25 150-158 Disease denotes COVID-19 MESH:C000657245
26 300-308 Disease denotes COVID-19 MESH:C000657245
27 335-343 Disease denotes infected MESH:D007239
28 344-352 Species denotes patients Tax:9606
29 483-491 Species denotes patients Tax:9606
30 575-583 Disease denotes infected MESH:D007239
31 589-597 Disease denotes COVID-19 MESH:C000657245
32 711-716 Disease denotes COVID MESH:C000657245
33 795-803 Disease denotes COVID-19 MESH:C000657245
34 936-941 Disease denotes COVID MESH:C000657245
35 998-1006 Disease denotes COVID-19 MESH:C000657245
36 1188-1195 Species denotes patient Tax:9606
37 1196-1203 Species denotes patient Tax:9606
38 1257-1265 Disease denotes COVID-19 MESH:C000657245
39 1352-1357 Disease denotes COVID MESH:C000657245
40 1496-1501 Disease denotes COVID MESH:C000657245
41 1572-1577 Disease denotes COVID MESH:C000657245
42 1787-1792 Disease denotes COVID MESH:C000657245
43 2061-2069 Disease denotes COVID-19 MESH:C000657245

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T1 0-119 Sentence denotes COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images.
T2 120-262 Sentence denotes The Coronavirus Disease 2019 (COVID-19) pandemic continues to have a devastating effect on the health and well-being of the global population.
T3 263-447 Sentence denotes A critical step in the fight against COVID-19 is effective screening of infected patients, with one of the key screening approaches being radiology examination using chest radiography.
T4 448-598 Sentence denotes It was found in early studies that patients present abnormalities in chest radiography images that are characteristic of those infected with COVID-19.
T5 599-896 Sentence denotes Motivated by this and inspired by the open source efforts of the research community, in this study we introduce COVID-Net, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public.
T6 897-1064 Sentence denotes To the best of the authors' knowledge, COVID-Net is one of the first open source network designs for COVID-19 detection from CXR images at the time of initial release.
T7 1065-1319 Sentence denotes We also introduce COVIDx, an open access benchmark dataset that we generated comprising of 13,975 CXR images across 13,870 patient patient cases, with the largest number of publicly available COVID-19 positive cases to the best of the authors' knowledge.
T8 1320-1712 Sentence denotes Furthermore, we investigate how COVID-Net makes predictions using an explainability method in an attempt to not only gain deeper insights into critical factors associated with COVID cases, which can aid clinicians in improved screening, but also audit COVID-Net in a responsible and transparent manner to validate that it is making decisions based on relevant information from the CXR images.
T9 1713-2131 Sentence denotes By no means a production-ready solution, the hope is that the open access COVID-Net, along with the description on constructing the open source COVIDx dataset, will be leveraged and build upon by both researchers and citizen data scientists alike to accelerate the development of highly accurate yet practical deep learning solutions for detecting COVID-19 cases and accelerate treatment of those who need it the most.