add LICENSE, README, package requirements

+# Deep GONet
+
+From the article entitled **GraphGONet: a self-explaining neural network encapsulating the Gene Ontology graph for phenotype prediction on gene expression** (submitted to Bioinformatics) by Victoria Bourgeais, Farida Zehraoui, and Blaise Hanczar.
+
+---
+
+## Description
+
+GraphGONet is a self-explaining neural network integrating the Gene Ontology into its hidden layers.
+
+## Get started
+
+The code is implemented in Python using the [PyTorch](https://pytorch.org/) framework v1.7.1 (see [requirements.txt](https://forge.ibisc.univ-evry.fr/vbourgeais/GraphGONet/blob/master/requirements.txt) for more details)
+
+### Dataset
+
+The full microarray dataset can be downloaded on ArrayExpress database under the id [E-MTAB-3732](https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-3732/). Here, you can find the pre-processed training and test sets:
+
+[training set](https://entrepot.ibisc.univ-evry.fr/f/5b57ab5a69de4f6ab26b/?dl=1)
+
+[test set](https://entrepot.ibisc.univ-evry.fr/f/057f1ffa0e6c4aab9bee/?dl=1) 
+
+<!-- Additional files for NN architecture: [filesforNNarch](https://entrepot.ibisc.univ-evry.fr/f/6f1c513798df41999b5d/?dl=1) -->
+
+TCGA dataset can be downloaded from [GDC portal](https://portal.gdc.cancer.gov/). 
+<!--
+Here, you can find the pre-processed training and test sets:
+
+[training set](https://entrepot.ibisc.univ-evry.fr/f/5b57ab5a69de4f6ab26b/?dl=1)
+
+[test set](https://entrepot.ibisc.univ-evry.fr/f/057f1ffa0e6c4aab9bee/?dl=1) 
+
+Additional files for NN architecture: [filesforNNarch](https://entrepot.ibisc.univ-evry.fr/f/6f1c513798df41999b5d/?dl=1)
+--> 
+
+### Usage
+
+<!--
+There exists 3 functions (flag *processing*): one is dedicated to the training of the model (*train*), another one to the evaluation of the model on the test set (*evaluate*), and the last one to the prediction of the outcomes of the samples from the test set (*predict*).
+-->
+
+#### 1) Train
+
+On the microarray dataset:
+```bash
+python GraphGONet.py --n_inputs=36834 --n_nodes=10663 --n_nodes_annotated=8249 --n_classes=1 --mask="top" --selection_ratio=0.01 --n_epochs=50 --es --patience=5 --class_weight 
+```
+
+On TCGA dataset:
+```bash
+python GraphGONet.py --n_inputs=18427 --n_nodes=10636 --n_nodes_annotated=8288 --n_classes=12 --mask="top" --selection_ratio=0.01 --n_epochs=50 --es --patience=5 --class_weight 
+```
+
+<!--
+#### 2) Evaluate
+
+
+```bash
+python DeepGONet.py --type_training="LGO" --alpha=1e-2 --EPOCHS=600 --is_training=False --restore=True --processing="evaluate"
+```
+
+#### 3) Predict
+
+
+```bash
+python DeepGONet.py --type_training="LGO" --alpha=1e-2 --EPOCHS=600 --is_training=False --restore=True --processing="predict"
+```
+
+
+The outcomes are saved into a numpy array.
+-->
+
+#### Help
+
+All the details about the command line flags can be provided by the following command:
+
+
+```bash
+python GraphGONet.py --help
+```
+
+For most of the flags, the default values can be employed. *log_dir* and *save_dir* can be modified to your own repositories. Only the flags in the command lines displayed have to be adjusted to achieve the desired objective.
+
+### Comparison with random selection
+
+On the microarray dataset:
+```bash
+python GraphGONet.py --n_inputs=36834 --n_nodes=10663 --n_nodes_annotated=8249 --n_classes=1 --mask="random" --selection_ratio=0.01 --n_epochs=50 --es --patience=5 --class_weight 
+```
+
+### Comparison with no selection
+
+On the microarray dataset:
+```bash
+python GraphGONet.py --n_inputs=36834 --n_nodes=10663 --n_nodes_annotated=8249 --n_classes=1 --n_epochs=50 --es --patience=5 --class_weight 
+```
+
+<!--
+###  Interpretation tool
+
+Please see the notebook entitled *Interpretation_tool.ipynb* to perform the biological interpretation of the results.
+-->
\ No newline at end of file

+Python==3.6.7
+captum==0.3.1
+jupyterlab==3.0.16
+matplotlib==3.3.4
+numpy==1.19.5
+networkx==2.5
+obonet==0.2.6
+pandas==1.1.5
+scikit-learn==0.24.2
+seaborn==0.11.1
+sklearn-pandas==2.2.0
+torch==1.7.1+cu110
+torch-cluster==1.5.8
+torch-geometric==1.6.3
+torch-scatter==2.0.5
+torch-sparse==0.6.8
+torch-spline-conv==1.2.0
+torchsummary==1.5.1
+torchtuples==0.2.0
+torchvision==0.8.2+cu110
+tqdm==4.56.0