Machine learning models in promoter prediction of microorganisms
To use this package, simply clone the project into a local folder
$ git clone https://github.com/Kleurenprinter/prompred.gitThe main module prompred.py is located in src/
The functionality of prompred can be directly accessed through the terminal. For more information on all implemented functions, simply type:
$ python prompred.py -hThe following script executes a gridsearch for the parameters alpha and gamma for a ridge regression using a third degree polynomial kernel, with features extracted over the [-7 12] and [-7 12] regions of the promoters (with respect to the 35- and 10-box)
$ python prompred.py GS -d ../data/external/mut_rand_mod_lib.csv -s -7 12 -7 12 -m ridge -k poly -g 3 --parL alpha gamma --parR 10 10 ├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── log.txt <- Log file holding a record all executed scripts
│ └── result_logger <- folder holding logs of the results of finished scripts
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ ├── prompred.py <- Main module, accessible through terminal
│ ├── log_utils.py <- functions to create log files
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org
Project based on the cookiecutter data science project template. #cookiecutterdatascience