AITom is an open-source platform for AI driven cellular electron cryo-tomography analysis.

Last update: Jan 02, 2023

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Overview

AITom

Introduction

AITom is an open-source platform for AI driven cellular electron cryo-tomography analysis.

AITom is originated from the tomominer library, adapted from an extended version of the tomominer library, developed at Alber Lab.

About us

Xulab at Carnegie Mellon University Computational Biology Department

Code and data for projects developed and maintained by Xu Lab and collaborators.

The research related to the code and data can be found at http://cs.cmu.edu/~mxu1

Background

Nearly every major process in a cell is orchestrated by the interplay of macromolecular assemblies, which often coordinate their actions as functional modules in biochemical pathways. To proceed efficiently, this interplay between different macromolecular machines often requires a distinctly nonrandom spatial organization in the cell. With the recent revolutions in cellular Cryo-Electron Tomography (Cryo-ET) imaging technologies, it is now possible to generate 3D reconstructions of cells in hydrated, close to native states at submolecular resolution.

Research

We are developing computational analysis techniques for processing large amounts of Cryo-ET data to reconstruct, detect, classify, recover, and spatially model different cellular components. We utilize state-of-the-art machine learning (including deep learning) approaches to design Cryo-ET specific data analysis and modeling algorithms. Our research automates the cellular structure discovery and will lead to new insights into the basic molecular biology and medical applications.

De novo structural mining pipeline results: (a). A slice of a rat neuron tomogram, (b). Recovered patterns (from left to right): mitochondrial membrane, Ribosome-like pattern, ellipsoid of strong signals, TRiC-like pattern, borders of ice crystal, (c). Pattern mining results embedded, (d). Individual patterns embedded.

Cite AITom

Technical report: AITom: Open-source AI platform for cryo-electron Tomography data analysis

@article{zeng2019aitom,
  title={AITom: Open-source AI platform for cryo-electron Tomography data analysis},
  author={Zeng, Xiangrui and Xu, Min},
  journal={arXiv preprint arXiv:1911.03044},
  year={2019}
}

Funding

Comments

make FAML running

entry function aitom.average.ml.faml.faml.test_EM_real_data

The method is based on the following paper:

Zhao Y, Zeng X, Guo Q, Xu M. An integration of fast alignment and maximum-likelihood methods for electron subtomogram averaging and classification. ISMB 2018. Bioinformatics. 2018 Jul 1; 34(13): i227–i236. doi:10.1093/bioinformatics/bty267. arXiv:1804.01203

Let's first fix the bugs to make the program run.

@ijinjay for technical questions, can consult with @xiangruz .
ToDo

opened by xulabs 10
Added Django server as the backend and a basic upload
Hello,

I am Abhinav Agarwal.

In this pull request I have added:

Django as the backend http server for AITom GUI.

Added functionality to upload a .mrc file and view it in the browser.
opened by anshabhi 7
implement saliency detection based particle picking

According to Feature Decomposition Based Saliency Detection in Electron Cryo-Tomograms. https://arxiv.org/abs/1801.10562

the supervoxel computation could be implemented on GPU using numba.

opened by xulabs 3

Facing problem running ProtoNet

Greetings,

I am currently facing a problem whenever I try to work with ProtoNet. As per the documentation, if I run python run_train.py, I get the following error.

Traceback (most recent call last):
  File "run_train.py", line 4, in <module>
    from train import main
  File "/shared/home/v_ajmain_yasar_ahmed_sahil/aitom/aitom/classify/deep/supervised/cnn/few_shot/protonet/train.py", line 20, in <module>
    from protonets.utils import data as data_utils
  File "/shared/home/v_ajmain_yasar_ahmed_sahil/aitom/aitom/classify/deep/supervised/cnn/few_shot/protonet/protonets/utils/data.py", line 1, in <module>
    import protonets.data
ModuleNotFoundError: No module named 'protonets.data'

Apparently, there is no protonets.data file under the directory protonets/utils where the file data.py resides. Am I missing something here?

Thank you.

opened by FromSaffronCity 2

integrate openset method into AITom

write 10 - 20 slides and add to google drive, need to complete before Sep 15

The following tasks are not urgent, can be performed after the AAAI deadline

add the code under following module aitom/aitom/classify/deep/supervised/cnn/openset

use existing functions in AITom as much as possible

write a tutorial and add to https://github.com/xulabs/aitom_doc/blob/master/tutorials/011_openset_learning.py

prepare page and figures to be added to https://github.com/xulabs/projects/openset_learning/readme.md

opened by xulabs 2
Make autoencoder working

add autoencoder code under

aitom.classify.deep.unsupervised.autoencoder

according to https://github.com/xulabs/projects/tree/master/autoencoder

and make it working.

write a tutorial on how to use it, and put the tutorial in aitom/tutorials/006_autoencoder.py
ToDo

opened by xulabs 2
first commit

Pytorch Implementation of CECT Segmentation Methods

[1] 2020_PUB-SalNet_ A Pre-Trained Unsupervised Self-Aware Backpropagation Network for Biomedical Salient Segmentation

[2] Domain Randomization for Macromolecule Structure Classification and Segmentation in Electron Cyro-tomograms

[3] Multi-task Learning for Macromolecule Classification, Segmentation and Coarse Structural Recovery in Cryo-Tomography

opened by Huiyu-Li 1
tool for cropping tomogram
We can use numpy to crop a volume, but if we save the volume into a mrc file, the header information is incorrect. To solve this problem there are two possible ways:

try to find a command line tool to do such cropping. This command line tool may exist in bsoft package

write our own code to correct the header and write into the mrc file using the mrcfile package.

Following is how to crop a tomogram using bshow: To cut a subvolume using bshow: 1) choose micrograph / pick particles 2) set box size 3) in boxes menu, choose extract particles 4) may check individual output files. 5) use micrograph / write parameter file. You can first cut according to the low passed filtered map, save the cutted subvolume and star file, then modified star file and load it with the unfiltered tomogram. Then cut the unfiltered tomogram and save the subvolume.
enhancement
opened by xulabs 1
test deep learning based classification

See if you can understand following tutorial and successfully run it

https://github.com/xulabs/aitom/blob/master/tutorials/010_deep_learning_subtomogram_classification.py

opened by xulabs 1
some import errors

1： https://github.com/xulabs/aitom/blob/master/aitom/pick/dog/particle_picking_dog__util.py ImportError: No module named 'aitom.tomominer.image.vol.partition' in line 43. Maybe it should be changed to 'aitom.image.vol.partition' ? 2: https://github.com/xulabs/aitom/blob/master/aitom/filter/gaussian.py NameError: name 'fftn' is not defined in line 34. It seems that some modules or functions have not been imported. Currently, tutorial_008 uses dog_smooth instead of dog_smooth__large_map.
bug

opened by zhuzhenxi 1
"aitom.filter.gaussian.dog_smooth__large_map" not found

In line 91 of https://github.com/xulabs/aitom/blob/master/aitom/pick/dog/particle_picking_dog__util.py

No function named "gaussian.dog_smooth__large_map" in https://github.com/xulabs/aitom/blob/master/aitom/filter/gaussian.py
bug

opened by zhuzhenxi 1
Bump setuptools from 44.0.0 to 65.5.1 in /aitom/align/deep/jim/2D
Bumps setuptools from 44.0.0 to 65.5.1.

Release notes

Sourced from setuptools's releases.

v65.5.1

No release notes provided.

v65.5.0

No release notes provided.

v65.4.1

No release notes provided.

v65.4.0

No release notes provided.

v65.3.0

No release notes provided.

v65.2.0

No release notes provided.

v65.1.1

No release notes provided.

v65.1.0

No release notes provided.

v65.0.2

No release notes provided.

v65.0.1

No release notes provided.

v65.0.0

No release notes provided.

v64.0.3

No release notes provided.

v64.0.2

No release notes provided.

v64.0.1

No release notes provided.

v64.0.0

No release notes provided.

v63.4.3

No release notes provided.

v63.4.2

No release notes provided.

... (truncated)

Changelog

Sourced from setuptools's changelog.

v65.5.1

Misc ^^^^

#3638: Drop a test dependency on the mock package, always use :external+python:py:mod:unittest.mock -- by :user:hroncok

#3659: Fixed REDoS vector in package_index.

v65.5.0

Changes ^^^^^^^

#3624: Fixed editable install for multi-module/no-package src-layout projects.

#3626: Minor refactorings to support distutils using stdlib logging module.

Documentation changes ^^^^^^^^^^^^^^^^^^^^^

#3419: Updated the example version numbers to be compliant with PEP-440 on the "Specifying Your Project’s Version" page of the user guide.

Misc ^^^^

#3569: Improved information about conflicting entries in the current working directory and editable install (in documentation and as an informational warning).

#3576: Updated version of validate_pyproject.

v65.4.1

Misc ^^^^

#3613: Fixed encoding errors in expand.StaticModule when system default encoding doesn't match expectations for source files.

#3617: Merge with pypa/[email protected] including fix for pypa/distutils#181.

v65.4.0

Changes ^^^^^^^

#3609: Merge with pypa/[email protected] including support for DIST_EXTRA_CONFIG in pypa/distutils#177.

v65.3.0

... (truncated)

Commits

a462cb5 Bump version: 65.5.0 → 65.5.1

de35d8b Merge pull request #3656 from bmorris3/typos

58e23de Update changelog. Ref #3659.

43a9c9b Limit the amount of whitespace to search/backtrack. Fixes #3659.

5791343 Add test capturing failed expectation. Ref #3659.

1f97905 ⚫ Fade to black.

6254567 Remove workaround for emacs.

729b180 ⚫ Fade to black.

c068081 Typo corrections

f777a40 Suppress deprecation warning in --rsyncdir. Workaround for #3655.

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dependencies
opened by dependabot[bot] 0
Bump wheel from 0.34.2 to 0.38.1 in /aitom/align/deep/jim/2D
Bumps wheel from 0.34.2 to 0.38.1.

Changelog

Sourced from wheel's changelog.

Release Notes

UNRELEASED

Updated vendored packaging to 22.0

0.38.4 (2022-11-09)

Fixed PKG-INFO conversion in bdist_wheel mangling UTF-8 header values in METADATA (PR by Anderson Bravalheri)

0.38.3 (2022-11-08)

Fixed install failure when used with --no-binary, reported on Ubuntu 20.04, by removing setup_requires from setup.cfg

0.38.2 (2022-11-05)

Fixed regression introduced in v0.38.1 which broke parsing of wheel file names with multiple platform tags

0.38.1 (2022-11-04)

Removed install dependency on setuptools

The future-proof fix in 0.36.0 for converting PyPy's SOABI into a abi tag was faulty. Fixed so that future changes in the SOABI will not change the tag.

0.38.0 (2022-10-21)

Dropped support for Python < 3.7

Updated vendored packaging to 21.3

Replaced all uses of distutils with setuptools

The handling of license_files (including glob patterns and default values) is now delegated to setuptools>=57.0.0 (#466). The package dependencies were updated to reflect this change.

Fixed potential DoS attack via the WHEEL_INFO_RE regular expression

Fixed ValueError: ZIP does not support timestamps before 1980 when using SOURCE_DATE_EPOCH=0 or when on-disk timestamps are earlier than 1980-01-01. Such timestamps are now changed to the minimum value before packaging.

0.37.1 (2021-12-22)

Fixed wheel pack duplicating the WHEEL contents when the build number has changed (#415)

Fixed parsing of file names containing commas in RECORD (PR by Hood Chatham)

0.37.0 (2021-08-09)

Added official Python 3.10 support

Updated vendored packaging library to v20.9

... (truncated)

Commits

6f1608d Created a new release

cf8f5ef Moved news item from PR #484 to its proper place

9ec2016 Removed install dependency on setuptools (#483)

747e1f6 Fixed PyPy SOABI parsing (#484)

7627548 [pre-commit.ci] pre-commit autoupdate (#480)

7b9e8e1 Test on Python 3.11 final

a04dfef Updated the pypi-publish action

94bb62c Fixed docs not building due to code style changes

d635664 Updated the codecov action to the latest version

fcb94cd Updated version to match the release

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dependencies
opened by dependabot[bot] 0
Bump certifi from 2020.6.20 to 2022.12.7 in /aitom/align/deep/jim/2D
Bumps certifi from 2020.6.20 to 2022.12.7.

Commits

9e9e840 2022.12.07

b81bdb2 2022.09.24

939a28f 2022.09.14

aca828a 2022.06.15.2

de0eae1 Only use importlib.resources's new files() / Traversable API on Python ≥3.11 ...

b8eb5e9 2022.06.15.1

47fb7ab Fix deprecation warning on Python 3.11 (#199)

b0b48e0 fixes #198 -- update link in license

9d514b4 2022.06.15

4151e88 Add py.typed to MANIFEST.in to package in sdist (#196)

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dependencies
opened by dependabot[bot] 0
Bump pillow from 9.0.1 to 9.3.0 in /aitom/align/deep/jim/2D
Bumps pillow from 9.0.1 to 9.3.0.

Release notes

Sourced from pillow's releases.

9.3.0

https://pillow.readthedocs.io/en/stable/releasenotes/9.3.0.html

Changes

Initialize libtiff buffer when saving #6699 [@radarhere]

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [@wiredfool]

Inline fname2char to fix memory leak #6329 [@nulano]

Fix memory leaks related to text features #6330 [@nulano]

Use double quotes for version check on old CPython on Windows #6695 [@hugovk]

GHA: replace deprecated set-output command with GITHUB_OUTPUT file #6697 [@nulano]

Remove backup implementation of Round for Windows platforms #6693 [@cgohlke]

Upload fribidi.dll to GitHub Actions #6532 [@nulano]

Fixed set_variation_by_name offset #6445 [@radarhere]

Windows build improvements #6562 [@nulano]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [@cgohlke]

Only use ASCII characters in C source file #6691 [@cgohlke]

Release Python GIL when converting images using matrix operations #6418 [@hmaarrfk]

Added ExifTags enums #6630 [@radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [@radarhere]

Added support for reading BMP images with RLE4 compression #6674 [@npjg]

Decode JPEG compressed BLP1 data in original mode #6678 [@radarhere]

pylint warnings #6659 [@marksmayo]

Added GPS TIFF tag info #6661 [@radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [@radarhere]

Do not attempt normalization if mode is already normal #6644 [@radarhere]

Fixed seeking to an L frame in a GIF #6576 [@radarhere]

Consider all frames when selecting mode for PNG save_all #6610 [@radarhere]

Don't reassign crc on ChunkStream close #6627 [@radarhere]

Raise a warning if NumPy failed to raise an error during conversion #6594 [@radarhere]

Only read a maximum of 100 bytes at a time in IMT header #6623 [@radarhere]

Show all frames in ImageShow #6611 [@radarhere]

Allow FLI palette chunk to not be first #6626 [@radarhere]

If first GIF frame has transparency for RGB_ALWAYS loading strategy, use RGBA mode #6592 [@radarhere]

Round box position to integer when pasting embedded color #6517 [@radarhere]

Removed EXIF prefix when saving WebP #6582 [@radarhere]

Pad IM palette to 768 bytes when saving #6579 [@radarhere]

Added DDS BC6H reading #6449 [@ShadelessFox]

Added support for opening WhiteIsZero 16-bit integer TIFF images #6642 [@JayWiz]

Raise an error when allocating translucent color to RGB palette #6654 [@jsbueno]

Moved mode check outside of loops #6650 [@radarhere]

Added reading of TIFF child images #6569 [@radarhere]

Improved ImageOps palette handling #6596 [@PososikTeam]

Defer parsing of palette into colors #6567 [@radarhere]

Apply transparency to P images in ImageTk.PhotoImage #6559 [@radarhere]

Use rounding in ImageOps contain() and pad() #6522 [@bibinhashley]

Fixed GIF remapping to palette with duplicate entries #6548 [@radarhere]

Allow remap_palette() to return an image with less than 256 palette entries #6543 [@radarhere]

Corrected BMP and TGA palette size when saving #6500 [@radarhere]

... (truncated)

Changelog

Sourced from pillow's changelog.

9.3.0 (2022-10-29)

Limit SAMPLESPERPIXEL to avoid runtime DOS #6700 [wiredfool]

Initialize libtiff buffer when saving #6699 [radarhere]

Inline fname2char to fix memory leak #6329 [nulano]

Fix memory leaks related to text features #6330 [nulano]

Use double quotes for version check on old CPython on Windows #6695 [hugovk]

Remove backup implementation of Round for Windows platforms #6693 [cgohlke]

Fixed set_variation_by_name offset #6445 [radarhere]

Fix malloc in _imagingft.c:font_setvaraxes #6690 [cgohlke]

Release Python GIL when converting images using matrix operations #6418 [hmaarrfk]

Added ExifTags enums #6630 [radarhere]

Do not modify previous frame when calculating delta in PNG #6683 [radarhere]

Added support for reading BMP images with RLE4 compression #6674 [npjg, radarhere]

Decode JPEG compressed BLP1 data in original mode #6678 [radarhere]

Added GPS TIFF tag info #6661 [radarhere]

Added conversion between RGB/RGBA/RGBX and LAB #6647 [radarhere]

Do not attempt normalization if mode is already normal #6644 [radarhere]

... (truncated)

Commits

d594f4c Update CHANGES.rst [ci skip]

909dc64 9.3.0 version bump

1a51ce7 Merge pull request #6699 from hugovk/security-libtiff_buffer

2444cdd Merge pull request #6700 from hugovk/security-samples_per_pixel-sec

744f455 Added release notes

0846bfa Add to release notes

799a6a0 Fix linting

00b25fd Hide UserWarning in logs

05b175e Tighter test case

13f2c5a Prevent DOS with large SAMPLESPERPIXEL in Tiff IFD

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dependencies
opened by dependabot[bot] 0
Some modules are missing

Hi, I am looking through the repository especially for the simulation of tomogram data. However, as mentioned in the issue #167, there are some missing modules not publicly available such as aitom_core. Is there any plan to share those modules in the near future, hopefully?

Best Regards!

opened by KJYoung 1
Gum-Net Training not improving with demo data

Hi,

I was testing the Gum-Net and for that used the provided demo data set. After around 30 epochs and around 15 hours of training I stopped it because there is no improvement in the loss function, please see below the logs of the training procedure.

Before finetuning: Rotation error: 1.7350925500744534 +/- 0.6650064011311111 Translation error: 8.442523177067761 +/- 3.44293514383784 ---------- Training Iteration 0 4/4 [==============================] - 1784s 404s/step - loss: 0.8216 Training Iteration 1 4/4 [==============================] - 1781s 405s/step - loss: 0.8218 Training Iteration 2 4/4 [==============================] - 1774s 404s/step - loss: 0.8251 Training Iteration 3 4/4 [==============================] - 1788s 406s/step - loss: 0.8274 Training Iteration 4 4/4 [==============================] - 1783s 405s/step - loss: 0.8334 Training Iteration 5 4/4 [==============================] - 1782s 405s/step - loss: 0.8201 Training Iteration 6 4/4 [==============================] - 1777s 405s/step - loss: 0.8250 Training Iteration 7 4/4 [==============================] - 1797s 407s/step - loss: 0.8310 Training Iteration 8 4/4 [==============================] - 1787s 407s/step - loss: 0.8336 Training Iteration 9 4/4 [==============================] - 1784s 406s/step - loss: 0.8207 Training Iteration 10 4/4 [==============================] - 1787s 406s/step - loss: 0.8258 Training Iteration 11 4/4 [==============================] - 1779s 405s/step - loss: 0.8235 Training Iteration 12 4/4 [==============================] - 1784s 406s/step - loss: 0.8296 Training Iteration 13 4/4 [==============================] - 1773s 402s/step - loss: 0.8271 Training Iteration 14 4/4 [==============================] - 1773s 403s/step - loss: 0.8199 Training Iteration 15 4/4 [==============================] - 1785s 406s/step - loss: 0.8315 Training Iteration 16 4/4 [==============================] - 1789s 407s/step - loss: 0.8264 Training Iteration 17 4/4 [==============================] - 1777s 405s/step - loss: 0.8336 Training Iteration 18 4/4 [==============================] - 1774s 403s/step - loss: 0.8299 Training Iteration 19 4/4 [==============================] - 1790s 407s/step - loss: 0.8303 Training Iteration 20 4/4 [==============================] - 1784s 406s/step - loss: 0.8244 Training Iteration 21 4/4 [==============================] - 1786s 407s/step - loss: 0.8242 Training Iteration 22 4/4 [==============================] - 1789s 406s/step - loss: 0.8245 Training Iteration 23 4/4 [==============================] - 1782s 406s/step - loss: 0.8253 Training Iteration 24 4/4 [==============================] - 1789s 405s/step - loss: 0.8258 Training Iteration 25 4/4 [==============================] - 1784s 406s/step - loss: 0.8238 Training Iteration 26 4/4 [==============================] - 1782s 405s/step - loss: 0.8200 Training Iteration 27 4/4 [==============================] - 1779s 405s/step - loss: 0.8282 Training Iteration 28 4/4 [==============================] - 1780s 405s/step - loss: 0.8251 Training Iteration 29 2/4 [==============>...............] - ETA: 19:00 - loss: 0.8142

Do you have any suggestions or explanation why the training with your demo dataset is not working? I did not change the source code.

Kind regards!

opened by kaysagit 4

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An implementation demo of the ICLR 2021 paper Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks in PyTorch.

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