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

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Deep Learningaitom
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.

Tutorials

Install

Question & Answer

Publications

Question & Answer

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

    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

    Added Django server as the backend and a basic upload

    Hello,

    I am Abhinav Agarwal.

    In this pull request I have added:

    1. Django as the backend http server for AITom GUI.
    2. Added functionality to upload a .mrc file and view it in the browser.
    opened by anshabhi 7
  • implement saliency detection based particle picking

    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

    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

    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

    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

    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

    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:

    1. try to find a command line tool to do such cropping. This command line tool may exist in bsoft package
    2. 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

    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

    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

    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

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    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 ^^^^

    v65.4.0

    Changes ^^^^^^^

    v65.3.0

    ... (truncated)

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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

    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
    • Additional commits viewable in compare view

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    dependencies 
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  • Bump certifi from 2020.6.20 to 2022.12.7 in /aitom/align/deep/jim/2D

    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.

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

    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.

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    9.3.0

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

    Changes

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    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)

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

    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

    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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knock_nott 0 May 04, 2022
A fast model to compute optical flow between two input images.

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Unofficial implementation of Point-Unet: A Context-Aware Point-Based Neural Network for Volumetric Segmentation

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An example of Scatterbrain implementation (combining local attention and Performer)

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HazyResearch 97 Jan 02, 2023
This project is based on RIFE and aims to make RIFE more practical for users by adding various features and design new models

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CMP 414/765 course repository for Spring 2022 semester

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LineBoard - Python+React+MySQL-白板即時系統改善人群行為

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A high-level Python library for Quantum Natural Language Processing

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Cambridge Quantum 315 Jan 01, 2023
A library for uncertainty quantification based on PyTorch

Torchuq [logo here] TorchUQ is an extensive library for uncertainty quantification (UQ) based on pytorch. TorchUQ currently supports 10 representation

TorchUQ 96 Dec 12, 2022
Based on Stockfish neural network(similar to LcZero)

MarcoEngine Marco Engine - interesnaya neyronnaya shakhmatnaya set', kotoraya ispol'zuyet metod samoobucheniya(dostizheniye khoroshoy igy putem proboy

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Repository of best practices for deep learning in Julia, inspired by fastai

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