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+# Gene Benchmark
+
+A benchmark for gene downstream tasks.
+
+The repository is divided into the following sections:
+
+* [gene_benchmark](./gene_benchmark/): The package itself, containing the scripts for extracting textual descriptions from NCBI, encoding textual descriptions and evaluation of model performance.
+
+* [notebooks](./notebooks/): Notebooks for creating the results figures and package usage examples.
+
+* [scripts](./scripts/): Scripts for description extraction, encoding, task creation and execution.
+
+* [tasks](./tasks/): The default directory that will be populated with all the tasks after running the task creation script.
+
+In depth explanation on each of the packages components can be found in the `gene_benchmark` directory.
+
+
+## Environment
+
+Using a virtual environment for all commands in this guide is strongly recommended.
+Both conda and vanilla venv environments are supported.
+
+```sh
+# create a conda enviornment "gene_benchmark" with Python version 3.11
+conda create -n gene_benchmark python=3.11
+
+# activate the enviornment before installing new packages
+conda activate gene_benchmark
+```
+
+## Installation
+
+### For non-developers
+The following command will install the repository as a Python package, and also attempt to install dependencies speficied in the setup.py file or the pyproject.toml. Note that the command does not clone the repositpry.
+
+```sh
+# assuming you have an SSH key set up on GitHub
+# this
+pip install "git+ssh://github.com/BiomedSciAI/gene-benchmark.git"
+
+# Change directory to the root of the cloned repository
+cd gene-benchmark
+
+# install from the local directory
+pip install -e .
+```
+
+### For developers
+
+
+```sh
+# Clone the cloned repository
+git clone git@github.com:BiomedSciAI/gene-benchmark.git
+
+# Change directory to the root of the cloned repository
+cd gene-benchmark
+
+pip install --upgrade pre-commit
+
+# install from the local directory
+pip install -e .
+
+pre-commit install
+```
+
+## Usage
+
+To evaluate your model on the tasks a few basic steps need to be done:
+### Set up
+1. Create the tasks: To create the tasks, run these commands in your terminal from the root directory:
+```sh
+python scripts/tasks_retrieval/gene2gene_task_creation.py --allow-downloads True
+python scripts/tasks_retrieval/Genecorpus_tasks_creation.py --allow-downloads True
+python scripts/tasks_retrieval/HLA_task_creation.py --allow-downloads True
+python scripts/tasks_retrieval/HPA_tasks_creation.py --allow-downloads True
+python scripts/tasks_retrieval/humantfs_task_creation.py --allow-downloads True
+python scripts/tasks_retrieval/Reactome_tasks_creation.py  --allow-downloads True
+python scripts/tasks_retrieval/uniprot_keyword_tasks_creation.py  --allow-downloads True
+```
+Now your [tasks](./tasks/) directory should be populated with subdirectories with the tasks names. Each subdirectory holds two .csv files, one with the gene symbols (entities.csv) and one with the labels (outcomes.csv). The shape of these csv files will defer based on the task type, for example for the multi class tasks, the outcomes will be a 2d matrix.
+
+2. Create your task yaml: The script for running the tasks can receive either the task names themselves or a .yaml file contacting the list of task names you wish to run. If you choose to create a .yaml file with the task names, create a separate file for each task type. For example for the binary tasks:
+
+```sh
+- TF vs non-TF
+- long vs short range TF
+- bivalent vs non-methylated
+- Lys4-only-methylated vs non-methylated
+- dosage sensitive vs insensitive TF
+- Gene2Gene
+- CCD Transcript
+- CCD Protein
+- N1 network
+- N1 targets
+- HLA class I vs class II
+```
+The example task configs can be found in [task_configs](./scripts/task_configs/)
+
+3. Create the model config file: This config file will hold the path to your models embeddings and the name you wish to use for your model. The structure of the file:
+```sh
+encoder:
+  class_name: PreComputedEncoder
+  class_args:
+    encoder_model_name: "/path/to/your/embeddings/my_models_embeddings.csv"
+model_name: my_model_name
+```
+Note that the script expects the embedding csv file to have a 'symbol' column with the gene symbols, this will be set as the index.
+
+### Run task
+Each task type (binary, categorical or multi-label) will be run separately.
+For example, for running the binary tasks the command is:
+
+sh```
+python scripts/run_task.py -t /path/to/task/yaml/base_binary.yaml -tf /tasks -m /path/to/model/config/model.yaml --output-file-name binary_tasks.csv
+```
+* For the other task types (categorical, regression or multi labe) you need to add `-s category/regression/multi`
+
+* When you are running the tasks on multiple models, and you would like them to be comparable, you can add a `excluded-symbols-file` input. This needs to be a path to a yaml file containing a list of gene names you would like to exclude.
+
+* To avoid getting errors during the cross validation due to class imbalance, you can add a threshold for the classes "-th" (for multi label)or "-cth" (for categorical)