PMC:7143846 / 575-905 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"20","span":{"begin":182,"end":189},"obj":"Disease"},{"id":"21","span":{"begin":191,"end":201},"obj":"Disease"}],"attributes":[{"id":"A20","pred":"tao:has_database_id","subj":"20","obj":"MESH:D001007"},{"id":"A21","pred":"tao:has_database_id","subj":"21","obj":"MESH:D000275"}],"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":"rs using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collect"}

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T5","span":{"begin":182,"end":201},"obj":"Disease"},{"id":"T6","span":{"begin":182,"end":189},"obj":"Disease"},{"id":"T8","span":{"begin":191,"end":201},"obj":"Disease"}],"attributes":[{"id":"A5","pred":"mondo_id","subj":"T5","obj":"http://purl.obolibrary.org/obo/MONDO_0041086"},{"id":"A6","pred":"mondo_id","subj":"T6","obj":"http://purl.obolibrary.org/obo/MONDO_0005618"},{"id":"A7","pred":"mondo_id","subj":"T6","obj":"http://purl.obolibrary.org/obo/MONDO_0011918"},{"id":"A8","pred":"mondo_id","subj":"T8","obj":"http://purl.obolibrary.org/obo/MONDO_0002050"}],"text":"rs using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collect"}

{"project":"LitCovid-PD-HP","denotations":[{"id":"T1","span":{"begin":182,"end":189},"obj":"Phenotype"},{"id":"T2","span":{"begin":191,"end":201},"obj":"Phenotype"}],"attributes":[{"id":"A1","pred":"hp_id","subj":"T1","obj":"http://purl.obolibrary.org/obo/HP_0000739"},{"id":"A2","pred":"hp_id","subj":"T2","obj":"http://purl.obolibrary.org/obo/HP_0000716"}],"text":"rs using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collect"}

{"project":"LitCovid-PD-GO-BP","denotations":[{"id":"T1","span":{"begin":86,"end":94},"obj":"http://purl.obolibrary.org/obo/GO_0007612"}],"text":"rs using the approach of Online Ecological Recognition (OER) based on several machine-learning predictive models. We calculated word frequency, scores of emotional indicators (e.g., anxiety, depression, indignation, and Oxford happiness) and cognitive indicators (e.g., social risk judgment and life satisfaction) from the collect"}