PMC:7510993 / 9489-9897 JSONTXT

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T60","span":{"begin":147,"end":155},"obj":"Disease"},{"id":"T61","span":{"begin":232,"end":240},"obj":"Disease"}],"attributes":[{"id":"A60","pred":"mondo_id","subj":"T60","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"},{"id":"A61","pred":"mondo_id","subj":"T61","obj":"http://purl.obolibrary.org/obo/MONDO_0100096"}],"text":"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]."}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T33","span":{"begin":171,"end":176},"obj":"http://purl.obolibrary.org/obo/NCBITaxon_9606"},{"id":"T34","span":{"begin":378,"end":379},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"}],"text":"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]."}

{"project":"LitCovid-PubTator","denotations":[{"id":"170","span":{"begin":171,"end":176},"obj":"Species"},{"id":"171","span":{"begin":147,"end":155},"obj":"Disease"},{"id":"172","span":{"begin":232,"end":240},"obj":"Disease"}],"attributes":[{"id":"A170","pred":"tao:has_database_id","subj":"170","obj":"Tax:9606"},{"id":"A171","pred":"tao:has_database_id","subj":"171","obj":"MESH:C000657245"},{"id":"A172","pred":"tao:has_database_id","subj":"172","obj":"MESH:C000657245"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"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]."}

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T2","span":{"begin":388,"end":396},"obj":"http://purl.obolibrary.org/obo/GO_0007612"}],"text":"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]."}

{"project":"LitCovid-sentences","denotations":[{"id":"T47","span":{"begin":0,"end":408},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"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]."}