Redo notebooks, adding DS and DR codes

README.md

@@ -1,7 +1,15 @@
 # Network Oriented Repurposing of Drugs (NORDic) package
 (c) Clémence Réda, 2022.
 
-[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7239048.svg)](https://doi.org/10.5281/zenodo.7239048)
+[NORDic](https://github.com/clreda/NORDic) is an open-source package which allows to focus on a network-oriented approach to identify regulatory mechanisms linked to a disease, master regulators, and to simulate drug effects on a network, and adaptively test drugs to perform drug repurposing. As such, it is comprised of four distinct parts:
+- **NORDic NI** identifies a disease-associated gene regulatory network (as a *Boolean network*) with its dynamics from several biological sources.
+- **NORDic PMR** detects master regulators in a Boolean network.
+- **NORDic DS** (since version 2.0.0) scores the effect of a treatment on a patient (the higher the score, the most promising the treatment) based on a Boolean network.
+- **NORDic DR** (since version 2.0.0) uses the routine in **NORDic DS** and a bandit algorithm to adaptively test treatments and perform drug repurposing.
+
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7239047.svg)](https://doi.org/10.5281/zenodo.7239047)
+
+To learn how to use the different methods, please check out the associated Jupyter notebooks.
 
 ## Citation
 
@@ -14,53 +22,9 @@ Due to the presence of copyrighted databases, the license for this code is [Attr
 ## Installation
 
 ```bash
-pip install NORDic==1.0.5 # release version
-pip install NORDic==1.0.6 # latest version
-```
-
-## Using the "refractory epilepsy" application
-
-Import the initial states from Mirza et al., 2017 and the M30 genes from Delahaye-Duriez et al., 2016
-
-```bash
-conda activate NORDic_env
-python3 download_Refractory_Epilepsy_Data.py
-conda deactivate
-```
-
-## Building a Boolean network
-
-You need to register to the [LINCS L1000 database](https://clue.io/developer-resources#apisection) and the [DisGeNet database](https://www.disgenet.org/) and write up the corresponding credentials and API keys to files 
-
-```python
-from NORDic.NORDic_NI.functions import network_identification
-solution = network_identification(file_folder, taxon_id, path_to_genes, ...)
+pip install NORDic==2.0.0 # latest version
 ```
 
-The final network solution is written to *<file_folder>solution.bnet*.
-
-## Detection of master regulators
-
-Using the filename in .bnet "network_name", the size k of the set of master regulators and the set of initial states states
-
-```python
-from NORDic.NORDic_PMR.functions import greedy
-S, spreads = greedy(network_name, k, states, ...)
-```
-
-The result file is named *application_regulators.csv*.
-
-## Network analysis with Cytoscape
-
-Network analyses are performed with Cytoscape 3.8.0. You need to download the module CytoCtrlAnalyser (version 1.0.0). Then run
-
-```python
-from NORDic.NORDic_NI.functions import solution2cytoscape
-solution2cytoscape(solution, file_folder+"solution_minimal_cytoscape")
-```
-
-which will create a style file (in .xml) and a network file readable by Cytoscape (in .sif). 
-
 ## Pull requests, issues, suggestions?
 
 clemence.reda@inserm.fr