{"id":23299605,"url":"https://github.com/j-i-l/tfb-prediction","last_synced_at":"2026-05-09T07:04:37.279Z","repository":{"id":63308167,"uuid":"500888188","full_name":"j-i-l/tfb-prediction","owner":"j-i-l","description":"Transcription factor binding prediction","archived":false,"fork":false,"pushed_at":"2022-06-15T10:35:41.000Z","size":148,"stargazers_count":2,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-04-06T20:47:56.142Z","etag":null,"topics":["bioinformatics","machine-learning","pandas","python","scikit-learn"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Transcription factor binding prediction\n\n## Preparation\n\n### Data location\n\nThe notebooks assume the presence of two files:\n\n- `./data/peak_data.txt`\n- `./data/shuffled_data.txt`\n\nFurther the folder `./data` should contain the subfolders\n\n- `./data/interim/`\n- `./data/engineered/`\n\n### Installation\n\nThe code is compatible with python \u003e=3.10, additional dependencies\nare listed in `requirements.txt`, to install them run:\n\n    pip install -r requirements.txt\n   \nin the root folder of this project.\n\n## Workflow\n\nsklearn's [pipelines](https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.Pipeline.html) are a great tool to condense an analysis and I would use them in most cases.\nHowever, I find them not to be ideal for demonstrating purposes, so in this project they will only come into play in the hyper-parameter tuning part in [Hyperparam_Tuning.ipynb](Hyperparam_Tuning.ipynb).\n\nThe notebooks should be run in the following order:\n\n- [Intro.ipynb](./Intro.ipynb): This is **optional** as it only provides some info about the problem at hand\n- [Processing_and_Cleaning.ipynb](Processing_and_Cleaning.ipynb): Creates pandas DataFrame's from the raw data (see [Data location](#data-location))\n- [Feature_Engineering.ipynb](Feature_Engineering.ipynb): Performs some feature engineering steps to convert the DNA sequences into usable feature vectors\n- [Model_Selection.ipynb](Model_Selection.ipynb): Performs a basic screening over some potential classifiers\n- [Hyperparam_Tuning.ipynb](Hyperparam_Tuning.ipynb): Performs hyper-parameter tuning, including some feature-engineering parameter, by cross-validating ml pipelines\n\n**Note:** The [Hyperparam_Tuning.ipynb](Hyperparam_Tuning.ipynb) includes feature engineering into a pipeline so it can work directly with the cleaned data generated in [Processing_and_Cleaning.ipynb](Processing_and_Cleaning.ipynb).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fj-i-l%2Ftfb-prediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fj-i-l%2Ftfb-prediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fj-i-l%2Ftfb-prediction/lists"}