Name | pypef JSON |
Version |
0.3.2
JSON |
| download |
home_page | https://github.com/niklases/PyPEF |
Summary | A command-line interface (CLI) tool for performing data-driven protein engineering by building machine learning (ML)-trained regression models from sequence variant fitness data (in CSV format) based on different techniques for protein sequence encoding. Next to building pure ML models, 'hybrid modeling' is also possible using a blended model optimized for predictive contributions of a statistical and an ML-based prediction. |
upload_time | 2023-08-17 06:38:23 |
maintainer | |
docs_url | None |
author | Niklas Siedhoff & Alexander-Maurice Illig |
requires_python | >= 3.9, < 3.12 |
license | CC BY-NC-SA 4.0 |
keywords |
pythonic
protein
engineering
framework
|
VCS |
|
bugtrack_url |
|
requirements |
No requirements were recorded.
|
Travis-CI |
No Travis.
|
coveralls test coverage |
No coveralls.
|
For detailed description including a short Jupyter Notebook-based tutorial please refer to the GitHub page.
Raw data
{
"_id": null,
"home_page": "https://github.com/niklases/PyPEF",
"name": "pypef",
"maintainer": "",
"docs_url": null,
"requires_python": ">= 3.9, < 3.12",
"maintainer_email": "",
"keywords": "Pythonic Protein Engineering Framework",
"author": "Niklas Siedhoff & Alexander-Maurice Illig",
"author_email": "n.siedhoff@biotec.rwth-aachen.de",
"download_url": "https://files.pythonhosted.org/packages/a5/96/509b540a89211d9db396762a239f84e10e2244ea235bf44a70a9aa6af19e/pypef-0.3.2.tar.gz",
"platform": null,
"description": "For detailed description including a short Jupyter Notebook-based tutorial please refer to the GitHub page.\n",
"bugtrack_url": null,
"license": "CC BY-NC-SA 4.0",
"summary": "A command-line interface (CLI) tool for performing data-driven protein engineering by building machine learning (ML)-trained regression models from sequence variant fitness data (in CSV format) based on different techniques for protein sequence encoding. Next to building pure ML models, 'hybrid modeling' is also possible using a blended model optimized for predictive contributions of a statistical and an ML-based prediction.",
"version": "0.3.2",
"project_urls": {
"Homepage": "https://github.com/niklases/PyPEF"
},
"split_keywords": [
"pythonic",
"protein",
"engineering",
"framework"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "8ed90782068885c54a03afe101489ee568c282f5d6cca6eb1f1658d98d260cee",
"md5": "b2acb5d49c061cb5f6f4b4b0aa1282d2",
"sha256": "b74320579944f4e0f215e203538bcf5d9df3787fae70bdace2ad9b3af3103dcb"
},
"downloads": -1,
"filename": "pypef-0.3.2-py3-none-any.whl",
"has_sig": false,
"md5_digest": "b2acb5d49c061cb5f6f4b4b0aa1282d2",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">= 3.9, < 3.12",
"size": 492830,
"upload_time": "2023-08-17T06:38:21",
"upload_time_iso_8601": "2023-08-17T06:38:21.919123Z",
"url": "https://files.pythonhosted.org/packages/8e/d9/0782068885c54a03afe101489ee568c282f5d6cca6eb1f1658d98d260cee/pypef-0.3.2-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "a596509b540a89211d9db396762a239f84e10e2244ea235bf44a70a9aa6af19e",
"md5": "5153117b49133e94983e9c4043d680c9",
"sha256": "09b53a236ecb8e425fd728bb0524faff70175713b7ea494348915ffdb2d28182"
},
"downloads": -1,
"filename": "pypef-0.3.2.tar.gz",
"has_sig": false,
"md5_digest": "5153117b49133e94983e9c4043d680c9",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">= 3.9, < 3.12",
"size": 241617,
"upload_time": "2023-08-17T06:38:23",
"upload_time_iso_8601": "2023-08-17T06:38:23.977242Z",
"url": "https://files.pythonhosted.org/packages/a5/96/509b540a89211d9db396762a239f84e10e2244ea235bf44a70a9aa6af19e/pypef-0.3.2.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2023-08-17 06:38:23",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "niklases",
"github_project": "PyPEF",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"requirements": [],
"lcname": "pypef"
}