EM-GAN is a computational tool, which enables capturing protein structure information from cryo-EM maps more effectively than raw maps. It is based on 3D deep learning. It is aimed to help protein structure modeling from cryo-EM maps.
Figure 1. The GAN architecture of EM-GAN. The detailed architecture of the generator and discriminator networks are shown. The blue arrows that connect the input of a ResNet block and a plus sign, which is the operator that simply add two matrices, is a skip connection.
data_prep/HLmapData_new 2788.mrc -c 0.16 > 2788_trimmap
python data_prep/dataset_final.py 2788_trimmap 2788_dataset 2788
echo ./2788_dataset > test_dataset_location
python test.py --input=test_dataset_location --G_res_blocks=15 --D_res_blocks=3 --G_path=model/Generator --D_path=model/Generator
EM-GAN is a free software for academic and non-commercial users.
It is released under the terms of the GNU General Public License Ver.3 (https://www.gnu.org/licenses/gpl-3.0.en.html).
Commercial users please contact dkihara@purdue.edu for alternate licensing.
Citation of the following reference should be included in any publication that uses data or results generated by EM-GAN program.
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