https://github.com/uibcdf/kinnetmt
Kinetic transitions networks or Conformational Markov Networks.
https://github.com/uibcdf/kinnetmt
Last synced: 2 months ago
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Kinetic transitions networks or Conformational Markov Networks.
- Host: GitHub
- URL: https://github.com/uibcdf/kinnetmt
- Owner: uibcdf
- License: mit
- Created: 2018-05-24T22:26:59.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2018-08-03T22:19:22.000Z (almost 7 years ago)
- Last Synced: 2025-01-23T03:14:18.504Z (4 months ago)
- Language: Python
- Homepage:
- Size: 33.8 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: License
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README
# KinNetMT
[](https://anaconda.org/uibcdf/kinnetmt)
[](https://anaconda.org/uibcdf/kinnetmt)
[](https://zenodo.org/badge/latestdoi/130507248)**[Installation](#installation)** |
**[Documentation](#documentation)** |
**[License](#license)** |
**[Credits](#credits)** |
**[Team](#team)**Kinnetic Networks' Multi-Tool
is part of KinNetMTs.
This library was thought as a humble frontend to make the life of a computational molecular biology laboratory, the UIBCDF, easier.
KinNetMT is design to cover specific needs or to speed up workflows when you are working with:-
Although KinNetMT was not concived to do what other tools do better, this
toolkit can be used alone to do few simple tasks.All credit should be given to the developers and mantainers of these former packages and the libraries they depend on.
## Installation
### Dependencies
### Conda
#### Updating
### GitHub
#### Updating
## Documentation
http://www.uibcdf.org/KinNetMT/
## License
## Credits
All credit should be given to the developers and mantainers of the following tools and dependencies:
...
## Team
### Responsables
Diego Prada Gracia
Liliana M. Moreno Vargas#### Contributors
...## Citation