PMC:7553147 / 5535-6158
Annnotations
LitCovid-PD-FMA-UBERON
| Id | Subject | Object | Predicate | Lexical cue | fma_id |
|---|---|---|---|---|---|
| T9 | 379-382 | Body_part | denotes | map | http://purl.org/sig/ont/fma/fma67847 |
| T10 | 448-451 | Body_part | denotes | Map | http://purl.org/sig/ont/fma/fma67847 |
LitCovid-PD-CLO
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T32 | 139-143 | http://purl.obolibrary.org/obo/CLO_0001185 | denotes | 2018 |
| T33 | 288-289 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
| T34 | 353-354 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
LitCovid-PD-GO-BP
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T1 | 413-421 | http://purl.obolibrary.org/obo/GO_0007612 | denotes | learning |
LitCovid-sentences
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T35 | 0-623 | Sentence | denotes | The basic elements of the workflow combine classic cryo-EM algorithms with recent improvements in particle picking (Sanchez-Garcia et al., 2018 ▸; Sanchez-Garcia, Segura et al., 2020 ▸; Wagner et al., 2019 ▸) and the key ideas of meta classifiers, which integrate multiple classifiers by a ‘consensus’ approach (Sorzano et al., 2020 ▸), and finish with a totally new approach to map post-processing based on deep learning that we term Deep cryo-EM Map Enhancer (DeepEMhancer; Sanchez-Garcia, Gomez-Blanco et al., 2020 ▸), which complements our previous proposal on local deblurring (Ramírez-Aportela, Vilas et al., 2020 ▸). |
LitCovid-PubTator
| Id | Subject | Object | Predicate | Lexical cue | tao:has_database_id |
|---|---|---|---|---|---|
| 56 | 116-130 | Disease | denotes | Sanchez-Garcia | MESH:C536767 |
| 57 | 147-161 | Disease | denotes | Sanchez-Garcia | MESH:C536767 |
| 58 | 462-474 | Disease | denotes | DeepEMhancer | |
| 59 | 476-490 | Disease | denotes | Sanchez-Garcia | MESH:C536767 |