 | Acta Crystallographica Section F Acta Crystallographica Section F STRUCTURAL BIOLOGY COMMUNICATIONS |
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![[Figure 1]](ih5009fig1mag.jpg)
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Figure 1
Architecture, training and benchmarking of CryoSift. (a) GUI of tkteach for 2D class labeling and basic architecture of the deep convolutional neural network of CryoSift. RELION or cryoSPARC 2D projections of 31 × 31 to 210 × 210 px input. Three data features from the mass estimator and three metadata features (FRC resolution, class distribution and pixel size) are also fed into the model, resulting in a predicted quality score. Example output of AP2 averages with grade-based labels (red). (b) 2D averages with predicted quality scores are grouped by protein, sampled across the full range of class-quality scores. (c) Mean-square error loss over epochs of training and validation with features. The inset shows the prediction error between true and predicted score as a confusion matrix (density on a log scale). (d) Overview of the CNN layers (details are given in Supplementary Fig. S2). |
![[Figure 1]](ih5009fig1.jpg)
| STRUCTURAL BIOLOGY COMMUNICATIONS |
ISSN: 2053-230X
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