| Name | LANDMarkClassifier JSON |
| Version |
2.1.1
JSON |
| download |
| home_page | None |
| Summary | LANDMark: An ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data |
| upload_time | 2024-04-27 05:00:27 |
| maintainer | None |
| docs_url | None |
| author | Teresita M. Porter, Michael Wright, G. Brian Golding |
| requires_python | >=3.10 |
| license | MIT License Copyright (c) 2022 Josip Rudar, Teresita M. Porter, Michael Wright, G.Brian Golding, Mehrdad Hajibabaei Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. |
| keywords |
classification
ecology
machine learning
multivariate statistics
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
### LANDMark
[](https://github.com/jrudar/LANDMark/actions/workflows/ci.yml)
Implementation of a decision tree ensemble which splits each node using learned linear and non-linear functions.
### Install
From PyPI:
```bash
pip install LANDMarkClassifier
```
From source:
```bash
git clone https://github.com/jrudar/LANDMark.git
cd LANDMark
pip install .
# or create a virtual environment
python -m venv venv
source venv/bin/activate
pip install .
```
## Interface
An overview of the API can be found [here](docs/API.md).
## Usage and Examples
Examples of how to use `LANDMark` can be found [here](notebooks/README.md).
## Contributing
To contribute to the development of `LANDMark` please read our [contributing guide](docs/CONTRIBUTING.md)
### Projects Using LANDMark
Rudar J, Kruczkiewicz P, Vernygora O, Golding GB, Hajibabaei M, Lung O. Sequence signatures
within the genome of SARS-CoV-2 can be used to predict host source. Microbiol Spectr.
2024 Apr 2;12(4):e0358423. doi: 10.1128/spectrum.03584-23. Epub 2024 Mar 4. PMID: 38436242.
Rudar J, Golding GB, Kremer SC, Hajibabaei M. Decision Tree Ensembles Utilizing Multivariate
Splits Are Effective at Investigating Beta Diversity in Medically Relevant 16S Amplicon
Sequencing Data. Microbiol Spectr. 2023 Mar 6;11(2):e0206522. doi: 10.1128/spectrum.02065-22.
Epub ahead of print. PMID: 36877086; PMCID: PMC10100742.
Rudar, J., Porter, T.M., Wright, M., Golding G.B., Hajibabaei, M. LANDMark: an ensemble
approach to the supervised selection of biomarkers in high-throughput sequencing data.
BMC Bioinformatics 23, 110 (2022). https://doi.org/10.1186/s12859-022-04631-z
### References
Rudar, J., Porter, T.M., Wright, M., Golding G.B., Hajibabaei, M. LANDMark: an ensemble
approach to the supervised selection of biomarkers in high-throughput sequencing data.
BMC Bioinformatics 23, 110 (2022). https://doi.org/10.1186/s12859-022-04631-z
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn:
Machine Learning in Python. Journal of Machine Learning Research. 2011;12:2825–30.
Kuncheva LI, Rodriguez JJ. Classifier ensembles with a random linear oracle.
IEEE Transactions on Knowledge and Data Engineering. 2007;19(4):500–8.
Geurts P, Ernst D, Wehenkel L. Extremely Randomized Trees. Machine Learning. 2006;63(1):3–42.
Raw data
{
"_id": null,
"home_page": null,
"name": "LANDMarkClassifier",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": null,
"keywords": "classification, ecology, machine learning, multivariate statistics",
"author": "Teresita M. Porter, Michael Wright, G. Brian Golding",
"author_email": "Josip Rudar <rudarj@uoguelph.ca>, Mehrdad Hajibabaei <mhajibab@uoguelph.ca>",
"download_url": "https://files.pythonhosted.org/packages/ce/a5/0473aceecb221022372241450f769fbafc431f8a969195f0c720f275c21b/landmarkclassifier-2.1.1.tar.gz",
"platform": null,
"description": "### LANDMark\n\n[](https://github.com/jrudar/LANDMark/actions/workflows/ci.yml)\n\nImplementation of a decision tree ensemble which splits each node using learned linear and non-linear functions.\n\n### Install\nFrom PyPI:\n\n```bash\npip install LANDMarkClassifier\n```\n\nFrom source:\n\n```bash\ngit clone https://github.com/jrudar/LANDMark.git\ncd LANDMark\npip install .\n# or create a virtual environment\npython -m venv venv\nsource venv/bin/activate\npip install .\n```\n\n## Interface\n\nAn overview of the API can be found [here](docs/API.md).\n\n## Usage and Examples\n\nExamples of how to use `LANDMark` can be found [here](notebooks/README.md).\n\n## Contributing\n\nTo contribute to the development of `LANDMark` please read our [contributing guide](docs/CONTRIBUTING.md)\n\n### Projects Using LANDMark\n\n Rudar J, Kruczkiewicz P, Vernygora O, Golding GB, Hajibabaei M, Lung O. Sequence signatures \n within the genome of SARS-CoV-2 can be used to predict host source. Microbiol Spectr. \n 2024 Apr 2;12(4):e0358423. doi: 10.1128/spectrum.03584-23. Epub 2024 Mar 4. PMID: 38436242.\n\n Rudar J, Golding GB, Kremer SC, Hajibabaei M. Decision Tree Ensembles Utilizing Multivariate \n Splits Are Effective at Investigating Beta Diversity in Medically Relevant 16S Amplicon \n Sequencing Data. Microbiol Spectr. 2023 Mar 6;11(2):e0206522. doi: 10.1128/spectrum.02065-22. \n Epub ahead of print. PMID: 36877086; PMCID: PMC10100742.\n\n Rudar, J., Porter, T.M., Wright, M., Golding G.B., Hajibabaei, M. LANDMark: an ensemble \n approach to the supervised selection of biomarkers in high-throughput sequencing data. \n BMC Bioinformatics 23, 110 (2022). https://doi.org/10.1186/s12859-022-04631-z\n\n### References\n\n Rudar, J., Porter, T.M., Wright, M., Golding G.B., Hajibabaei, M. LANDMark: an ensemble \n approach to the supervised selection of biomarkers in high-throughput sequencing data. \n BMC Bioinformatics 23, 110 (2022). https://doi.org/10.1186/s12859-022-04631-z\n\n Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: \n Machine Learning in Python. Journal of Machine Learning Research. 2011;12:2825\u201330. \n\n Kuncheva LI, Rodriguez JJ. Classifier ensembles with a random linear oracle. \n IEEE Transactions on Knowledge and Data Engineering. 2007;19(4):500\u20138. \n \n Geurts P, Ernst D, Wehenkel L. Extremely Randomized Trees. Machine Learning. 2006;63(1):3\u201342. \n\n",
"bugtrack_url": null,
"license": "MIT License Copyright (c) 2022 Josip Rudar, Teresita M. Porter, Michael Wright, G.Brian Golding, Mehrdad Hajibabaei Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.",
"summary": "LANDMark: An ensemble approach to the supervised selection of biomarkers in high-throughput sequencing data",
"version": "2.1.1",
"project_urls": {
"Bug Tracker": "https://github.com/jrudar/LANDMark/issues",
"Homepage": "https://github.com/jrudar/LANDMark",
"Repository": "https://github.com/jrudar/LANDMark.git"
},
"split_keywords": [
"classification",
" ecology",
" machine learning",
" multivariate statistics"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "37f7310c6577d7909311c7ba5386e805d4d3a39cc9e4187048f746519d6870dd",
"md5": "5173868cc9137af31e708e2f570ef621",
"sha256": "50f333dad79ed9476c2ce4136a89edfb60d12501515b6e253cf0470d448889df"
},
"downloads": -1,
"filename": "landmarkclassifier-2.1.1-py3-none-any.whl",
"has_sig": false,
"md5_digest": "5173868cc9137af31e708e2f570ef621",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.10",
"size": 16548,
"upload_time": "2024-04-27T05:00:24",
"upload_time_iso_8601": "2024-04-27T05:00:24.993318Z",
"url": "https://files.pythonhosted.org/packages/37/f7/310c6577d7909311c7ba5386e805d4d3a39cc9e4187048f746519d6870dd/landmarkclassifier-2.1.1-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "cea50473aceecb221022372241450f769fbafc431f8a969195f0c720f275c21b",
"md5": "141800903618f147a8dfb553375f5a5c",
"sha256": "acf569c63240a29bf3989596748169b96951f104ad0628948212422276c76c1e"
},
"downloads": -1,
"filename": "landmarkclassifier-2.1.1.tar.gz",
"has_sig": false,
"md5_digest": "141800903618f147a8dfb553375f5a5c",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.10",
"size": 327762,
"upload_time": "2024-04-27T05:00:27",
"upload_time_iso_8601": "2024-04-27T05:00:27.261361Z",
"url": "https://files.pythonhosted.org/packages/ce/a5/0473aceecb221022372241450f769fbafc431f8a969195f0c720f275c21b/landmarkclassifier-2.1.1.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-04-27 05:00:27",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "jrudar",
"github_project": "LANDMark",
"travis_ci": false,
"coveralls": false,
"github_actions": true,
"lcname": "landmarkclassifier"
}
