add README

+# Deep GONet
+
+From the article entitled **Deep GONet: Self-explainable deep neural network using Gene Ontology for phenotype prediction from gene expression data** (submitted to ECCB'20)
+
+---
+
+## Description
+
+Deep GONet is a self-explainable neural network integrating the Gene Ontology into its hierarchical architecture.
+
+## Get started
+
+The code is implemented in Python using the [Tensorflow](https://www.tensorflow.org/) framework v1.12 (see [requirements.txt](https://forge.ibisc.univ-evry.fr/vbourgeais/DeepGONet/blob/master/requirements.txt) for more details)
+
+### Dataset
+
+The full 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) 
+
+### Usage
+
+Deep GONet was achieved with the $L_{GO}$ regularization and the hyperparameter $\alpha=1e^{-2}$.  
+To replicate it, the command line flag *type_training* needs to be set to LGO (default value) and the command line flag *alpha* to $1e^{-2}$ (default value).  
+
+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
+
+
+```bash
+python DeepGONet.py --type_training="LGO" --alpha=1e-2 --EPOCHS=600 --is_training=True --display_step=10 --save=True --processing="train"
+```
+
+#### 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 DeepGONet.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 classical fully-connected network using L2 or L1 regularization terms
+
+It is possible to compare the model with L2,L1 regularization instead of LGO.
+
+
+```bash
+python DeepGONet.py --type_training="L2" --alpha=1e-2 --EPOCHS=600 --is_training=True --display_step=10 --save=True --processing="train"
+```
+
+
+```bash
+python DeepGONet.py --type_training="L1" --alpha=1e-2 --EPOCHS=600 --is_training=True --display_step=10 --save=True --processing="train"
+```
+
+Without regularization:
+
+
+```bash
+python DeepGONet.py --alpha=0 --EPOCHS=100 --is_training=True --display_step=5 --save=True --processing="train"
+```