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PMC:7510993 / 9489-9897 JSONTXT

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LitCovid-PD-MONDO

Id Subject Object Predicate Lexical cue mondo_id
T60 147-155 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096
T61 232-240 Disease denotes COVID-19 http://purl.obolibrary.org/obo/MONDO_0100096

LitCovid-PD-CLO

Id Subject Object Predicate Lexical cue
T33 171-176 http://purl.obolibrary.org/obo/NCBITaxon_9606 denotes human
T34 378-379 http://purl.obolibrary.org/obo/CLO_0001020 denotes a

LitCovid-PubTator

Id Subject Object Predicate Lexical cue tao:has_database_id
170 171-176 Species denotes human Tax:9606
171 147-155 Disease denotes COVID-19 MESH:C000657245
172 232-240 Disease denotes COVID-19 MESH:C000657245

LitCovid-PD-GO-BP

Id Subject Object Predicate Lexical cue
T2 388-396 http://purl.obolibrary.org/obo/GO_0007612 denotes learning

LitCovid-sentences

Id Subject Object Predicate Lexical cue
T47 0-408 Sentence denotes To ensure the robustness of our results, we investigated the relationship between various environmental variables (climate, host susceptibility to COVID-19, international human mobility, and socioeconomic factors) and the number of COVID-19 cases (per 1 million population) using the two different approaches: conventional multiple linear regression and random forest, which is a machine-learning model [15].