<h1 align="center">
<a href="https://anbai106.github.io/SOPNMF/">
<img src="https://anbai106.github.io/SOPNMF/images/sopnmf.png" alt="SOPNMF Logo" width="120" height="120">
</a>
<br/>
SOPNMF
</h1>
<p align="center"><strong>Stochastic orthogonally projective non-negative matrix factorization</strong></p>
<p align="center">
<a href="https://anbai106.github.io/SOPNMF/">Documentation</a>
</p>
## About the project
**SOPNMF** is the python implementation of the Matlab version of Orthogonal Projective Non-negative Matrix Factorization: [brainparts](https://github.com/asotiras/brainparts), and its stochastic extension.
> :warning: **The documentation of this software is currently under development**
## Citing this work
> Junhao, W.E.N., Abdulkadir, A., Satterthwaite, T.D., Robert-Fitzgerald, T., Chen, J., Schnack, H., Zanetti, M., Meisenzahl, E., Busatto, G., Crespo-Facorro, B. and Pantelis, C., 2022. **Novel genomic loci and pathways influence patterns of structural covariance in the human brain**. medRxiv. - [In review](https://www.medrxiv.org/content/10.1101/2022.07.20.22277727v1)
> Sotiras, A., Resnick, S.M. and Davatzikos, C., 2015. **Finding imaging patterns of structural covariance via non-negative matrix factorization**. Neuroimage, 108, pp.1-16. [doi:10.1016/j.neuroimage.2014.11.045](https://www.sciencedirect.com/science/article/pii/S1053811914009756?via%3Dihub)
## Publications around SOPNMF
> Wen, J., Varol, E., Sotiras, A., Yang, Z., Chand, G.B., Erus, G., Shou, H., Abdulkadir, A., Hwang, G., Dwyer, D.B. and Pigoni, A., 2022. Multi-scale semi-supervised clustering of brain images: deriving disease subtypes. Medical Image Analysis, 75, p.102304. - [Link](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=4Wq_FukAAAAJ&sortby=pubdate&citation_for_view=4Wq_FukAAAAJ:9ZlFYXVOiuMC)
Raw data
{
"_id": null,
"home_page": "https://github.com/anbai106/SOPNMF",
"name": "sopnmf",
"maintainer": "",
"docs_url": null,
"requires_python": "",
"maintainer_email": "",
"keywords": "",
"author": "junhao.wen",
"author_email": "junhao.wen89@email.com",
"download_url": "https://files.pythonhosted.org/packages/bc/53/35afc31368e77f86809b239fc77d9315b4cedefe1abbd95f697736ed2bf8/sopnmf-0.0.4.tar.gz",
"platform": null,
"description": "<h1 align=\"center\">\n <a href=\"https://anbai106.github.io/SOPNMF/\">\n <img src=\"https://anbai106.github.io/SOPNMF/images/sopnmf.png\" alt=\"SOPNMF Logo\" width=\"120\" height=\"120\">\n </a>\n <br/>\n SOPNMF\n</h1>\n\n<p align=\"center\"><strong>Stochastic orthogonally projective non-negative matrix factorization</strong></p>\n\n<p align=\"center\">\n <a href=\"https://anbai106.github.io/SOPNMF/\">Documentation</a>\n</p>\n\n## About the project\n**SOPNMF** is the python implementation of the Matlab version of Orthogonal Projective Non-negative Matrix Factorization: [brainparts](https://github.com/asotiras/brainparts), and its stochastic extension.\n\n> :warning: **The documentation of this software is currently under development**\n\n## Citing this work\n> Junhao, W.E.N., Abdulkadir, A., Satterthwaite, T.D., Robert-Fitzgerald, T., Chen, J., Schnack, H., Zanetti, M., Meisenzahl, E., Busatto, G., Crespo-Facorro, B. and Pantelis, C., 2022. **Novel genomic loci and pathways influence patterns of structural covariance in the human brain**. medRxiv. - [In review](https://www.medrxiv.org/content/10.1101/2022.07.20.22277727v1)\n\n> Sotiras, A., Resnick, S.M. and Davatzikos, C., 2015. **Finding imaging patterns of structural covariance via non-negative matrix factorization**. Neuroimage, 108, pp.1-16. [doi:10.1016/j.neuroimage.2014.11.045](https://www.sciencedirect.com/science/article/pii/S1053811914009756?via%3Dihub)\n\n## Publications around SOPNMF\n> Wen, J., Varol, E., Sotiras, A., Yang, Z., Chand, G.B., Erus, G., Shou, H., Abdulkadir, A., Hwang, G., Dwyer, D.B. and Pigoni, A., 2022. Multi-scale semi-supervised clustering of brain images: deriving disease subtypes. Medical Image Analysis, 75, p.102304. - [Link](https://scholar.google.com/citations?view_op=view_citation&hl=en&user=4Wq_FukAAAAJ&sortby=pubdate&citation_for_view=4Wq_FukAAAAJ:9ZlFYXVOiuMC)\n\n\n",
"bugtrack_url": null,
"license": "",
"summary": "Stochastic Orthogonal Projective Non-negative Matrix Factorization",
"version": "0.0.4",
"project_urls": {
"Homepage": "https://github.com/anbai106/SOPNMF"
},
"split_keywords": [],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "c6c5b90a3cf611437eadb597e7649af75f24fc963dd477606f2ded2e6a741cee",
"md5": "62485c75a569308a60bbb193554b1962",
"sha256": "0f9cfb0ece02b5806541e7bc14220422f9dc84444f5525c9bc86f099f94a5864"
},
"downloads": -1,
"filename": "sopnmf-0.0.4-py3-none-any.whl",
"has_sig": false,
"md5_digest": "62485c75a569308a60bbb193554b1962",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": null,
"size": 21914,
"upload_time": "2023-09-24T14:51:17",
"upload_time_iso_8601": "2023-09-24T14:51:17.493872Z",
"url": "https://files.pythonhosted.org/packages/c6/c5/b90a3cf611437eadb597e7649af75f24fc963dd477606f2ded2e6a741cee/sopnmf-0.0.4-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "bc5335afc31368e77f86809b239fc77d9315b4cedefe1abbd95f697736ed2bf8",
"md5": "df3e41319ee7c9056ec9ae3d72ddeb6f",
"sha256": "6fb0477d625e33ce2038c08787577d7ab45ed318c47733372c13f68a60e7ffd2"
},
"downloads": -1,
"filename": "sopnmf-0.0.4.tar.gz",
"has_sig": false,
"md5_digest": "df3e41319ee7c9056ec9ae3d72ddeb6f",
"packagetype": "sdist",
"python_version": "source",
"requires_python": null,
"size": 18866,
"upload_time": "2023-09-24T14:51:19",
"upload_time_iso_8601": "2023-09-24T14:51:19.111944Z",
"url": "https://files.pythonhosted.org/packages/bc/53/35afc31368e77f86809b239fc77d9315b4cedefe1abbd95f697736ed2bf8/sopnmf-0.0.4.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-09-24 14:51:19",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "anbai106",
"github_project": "SOPNMF",
"travis_ci": false,
"coveralls": false,
"github_actions": false,
"requirements": [
{
"name": "numpy",
"specs": [
[
"==",
"1.17.2"
]
]
},
{
"name": "scipy",
"specs": [
[
"==",
"1.3.1"
]
]
},
{
"name": "pandas",
"specs": []
},
{
"name": "nibabel",
"specs": []
},
{
"name": "tensorboardX",
"specs": []
},
{
"name": "pytorch",
"specs": []
}
],
"lcname": "sopnmf"
}
JSON
