PMC:7143846 / 7779-8515
Annnotations
LitCovid-PubTator
| Id | Subject | Object | Predicate | Lexical cue | tao:has_database_id |
|---|---|---|---|---|---|
| 95 | 276-302 | Disease | denotes | deep learning technologies | MESH:D007859 |
| 96 | 336-343 | Disease | denotes | anxiety | MESH:D001007 |
| 97 | 345-355 | Disease | denotes | depression | MESH:D000275 |
LitCovid-PD-MONDO
| Id | Subject | Object | Predicate | Lexical cue | mondo_id |
|---|---|---|---|---|---|
| T32 | 336-355 | Disease | denotes | anxiety, depression | http://purl.obolibrary.org/obo/MONDO_0041086 |
| T33 | 336-343 | Disease | denotes | anxiety | http://purl.obolibrary.org/obo/MONDO_0005618|http://purl.obolibrary.org/obo/MONDO_0011918 |
| T35 | 345-355 | Disease | denotes | depression | http://purl.obolibrary.org/obo/MONDO_0002050 |
LitCovid-PD-CLO
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T37 | 159-165 | http://purl.obolibrary.org/obo/CLO_0001658 | denotes | active |
| T38 | 605-606 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
| T39 | 692-695 | http://purl.obolibrary.org/obo/CLO_0051582 | denotes | has |
LitCovid-PD-CHEBI
| Id | Subject | Object | Predicate | Lexical cue | chebi_id |
|---|---|---|---|---|---|
| T2 | 544-553 | Chemical | denotes | indicator | http://purl.obolibrary.org/obo/CHEBI_47867 |
LitCovid-PD-HP
| Id | Subject | Object | Predicate | Lexical cue | hp_id |
|---|---|---|---|---|---|
| T8 | 336-343 | Phenotype | denotes | anxiety | http://purl.obolibrary.org/obo/HP_0000739 |
| T9 | 345-355 | Phenotype | denotes | depression | http://purl.obolibrary.org/obo/HP_0000716 |
LitCovid-PD-GO-BP
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T13 | 281-289 | http://purl.obolibrary.org/obo/GO_0007612 | denotes | learning |
LitCovid-sentences
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T57 | 0-178 | Sentence | denotes | After feature extraction, we used the psychological prediction model [25] obtained from the preliminary training to predict the psychological profile of these active Weibo users. |
| T58 | 179-470 | Sentence | denotes | These predictive models are tools developed for online psychology research based on big data and deep learning technologies, including emotional indicators (anxiety, depression, indignation, and Oxford happiness), cognitive indicators (social risk judgment and life satisfaction), and so on. |
| T59 | 471-565 | Sentence | denotes | Figure 1 portrays the procedure from feature extraction to psychological indicator prediction. |
| T60 | 566-654 | Sentence | denotes | All the prediction models have reached a moderate correlation with questionnaire scores. |
| T61 | 655-736 | Sentence | denotes | The feasibility of predictive models has been repeatedly demonstrated [26,27,28]. |
2_test
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| 32204411-28059682-49451145 | 732-734 | 28059682 | denotes | 28 |
| T27610 | 732-734 | 28059682 | denotes | 28 |