| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T96 |
0-183 |
Sentence |
denotes |
In this work, we used two volume post-processing approaches that both depart substantially from the traditional approach in the field, which is the application of global B-sharpening. |
| T97 |
184-309 |
Sentence |
denotes |
One of the approaches is our previously introduced LocalDeblur sharpening method (Ramírez-Aportela, Maluenda et al., 2020 ▸). |
| T98 |
310-423 |
Sentence |
denotes |
The second approach is a totally new method based on deep learning (Sanchez-Garcia, Gomez-Blanco et al., 2020 ▸). |
| T99 |
424-728 |
Sentence |
denotes |
Concentrating on the latter, this method, DeepEMhancer, relies on a common approach in modern pattern recognition in which a convolutional neural network (CNN) is trained on a known data set comprised of pairs of data points and targets, with the aim of predicting the targets for new unseen data points. |
| T100 |
729-925 |
Sentence |
denotes |
In this case, the training was performed by presenting the CNN with pairs of cryo-EM maps collected from the EMDB and maps derived from the structural models associated with the experimental maps. |
| T101 |
926-1071 |
Sentence |
denotes |
As a result, our CNN learned how to obtain much cleaner and detailed versions of the experimental cryo-EM maps, improving their interpretability. |
