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.github/workflows/build_package.yml

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.github/workflows/publish-docker.yml

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# Configures this workflow to run every time a release is published
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push:
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- main
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.github/workflows/publish_docs.yml

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.github/workflows/python-package.yml

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.github/workflows/python-publish.yml

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.github/workflows/run_tests.yml

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.readthedocs.yml

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Dockerfile

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README.md

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# DrEvalPy: Python Cancer Cell Line Drug Response Prediction Suite
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[![PyPI version](https://img.shields.io/pypi/v/drevalpy.svg)](https://pypi.org/project/drevalpy/)
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![Python versions](https://img.shields.io/pypi/pyversions/drevalpy)
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[![License](https://img.shields.io/github/license/daisybio/drevalpy)](https://opensource.org/licenses/GPL3)
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[![Read the Docs](https://img.shields.io/readthedocs/drevalpy/latest.svg?label=Read%20the%20Docs)](https://drevalpy.readthedocs.io/)
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[![Test status](https://github.com/daisybio/drevalpy/actions/workflows/run_tests.yml/badge.svg)](https://github.com/daisybio/drevalpy/actions?workflow=Tests)
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[![Precommit](https://img.shields.io/badge/pre--commit-enabled-brightgreen?logo=pre-commit&logoColor=white)](https://github.com/pre-commit/pre-commit)
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[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
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Focus on Innovating Your Models — DrEval Handles the Rest!
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- DrEval is a toolkit that ensures drug response prediction evaluations are statistically sound, biologically meaningful, and reproducible.
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By contributing your model to the DrEval catalog, you can increase your work's exposure, reusability, and transferability.
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![DrEval](https://github.com/daisybio/drevalpy/blob/main/assets/dreval.png)
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![DrEval](assets/dreval.png)
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Use DrEval to Build Drug Response Models That Have an Impact
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```
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From Source:
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Using docker:
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```bash
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```
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```bash
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conda env create -f models/SimpleNeuralNetwork/env.yml
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pip install .
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cd drevalpy
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## Quickstart
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To run models from the catalog, you can run:
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python run_suite.py --run_id my_first_run --models ElasticNet SimpleNeuralNetwork --dataset GDSC1 --test_mode LCO
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This will train and tune a neural network and an elastic net model on a subset of gene expression features and drug fingerprint features to predict IC50 values of the GDSC1 database. It will evaluate in "LCO" which is the leave-cell-line-out splitting strategy using 5 fold cross validation.
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features to predict IC50 values of the GDSC2 database. It will evaluate in "LCO" which is the leave-cell-line-out
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splitting strategy using 5 fold cross validation.
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The results will be stored in
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```bash

assets/dreval.png

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