Nina Miolane, Julien Martel, Gordon Wetzstein, Axel Levy, Frédéric Poitevin, Youssef Nashed, Ariana Peck, Daniel Ratner, Mike Dunne
We lifted 13 functions out of this paper's own repositories and ran 12 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| compSPI/cryoAI | canonical | 12 of 13 |
| Function | Status | Where it lives |
|---|---|---|
| euler_angles2matrix | Ran | compSPI/cryoAI/src/geom_utils.py code served (permissive licence) · get_code("ffbf3a813bdbc33e") |
| fourier_to_primal_2D | Ran | compSPI/cryoAI/src/ctf_utils.py code served (permissive licence) · get_code("0fe6c61e1eda758a") |
| get_power | Ran | compSPI/cryoAI/src/dataio.py code served (permissive licence) · get_code("dcd0b9b5b77266e0") |
| get_ref_matrix | Ran | compSPI/cryoAI/src/geom_utils.py code served (permissive licence) · get_code("88117ba9f8e95257") |
| get_rotation_accuracy | Ran | compSPI/cryoAI/src/geom_utils.py code served (permissive licence) · get_code("b14237dcb152fb9a") |
| imag | Ran | compSPI/cryoAI/src/loss_utils.py code served (permissive licence) · get_code("4b1849f54b1662c2") |
| layer_factory | Ran | compSPI/cryoAI/src/ml_modules.py code served (permissive licence) · get_code("44016b212e533147") |
| pairwise_cos_sim | Ran | compSPI/cryoAI/src/loss_utils.py code served (permissive licence) · get_code("9a435ee22ee796f8") |
| primal_to_fourier_2D | Ran | compSPI/cryoAI/src/ctf_utils.py code served (permissive licence) · get_code("af24a395eb0623d7") |
| primal_to_fourier_3D | Ran | compSPI/cryoAI/src/ctf_utils.py code served (permissive licence) · get_code("39ca2f4770ffd96b") |
| real | Ran | compSPI/cryoAI/src/loss_utils.py code served (permissive licence) · get_code("d39b04695bee5dd0") |
| which_half_space | Ran | compSPI/cryoAI/src/ml_modules.py code served (permissive licence) · get_code("a0706e978251531b") |
| build_model_fouriernet | Not yet run | compSPI/cryoAI/src/ml_modules.py code served (permissive licence) · get_code("aa16d674e221a373") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Cryo-electron microscopy (cryo-EM) has become a tool of fundamental importance in structural biology, helping us understand the basic building blocks of life. The algorithmic challenge of cryo-EM is to jointly estimate the unknown 3D poses and the 3D electron scattering potential of a biomolecule from millions of extremely noisy 2D images. Existing reconstruction algorithms, however, cannot easily keep pace with the rapidly growing size of cryo-EM datasets due to their high computational and memory cost. We introduce cryoAI, an ab initio reconstruction algorithm for homogeneous conformations that uses direct gradient-based optimization of particle poses and the electron scattering potential from single-particle cryo-EM data. CryoAI combines a learned encoder that predicts the poses of each particle image with a physicsbased decoder to aggregate each particle image into an implicit representation of the scattering potential volume. This volume is stored in the Fourier domain for computational efficiency and leverages a modern coordinate network architecture for memory efficiency. Combined with a symmetric loss function, this framework achieves results of a quality on par with state-of-the-art cryo-EM solvers for both simulated and experimental data, one order of magnitude faster for large datasets and with significantly lower memory requirements than existing methods.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2203.08138")
get_code_for_paper("2203.08138")
have("2203.08138")
Connect an agent — have() is free.