readme

README.md

@@ -24,7 +24,7 @@ To run the approach with the default parameters, just run the following command
 ```python
 python3 run_mem.py <dataset>
 ```
-with <dataset> corresponding to the dataset file and repository.
+with \<dataset\> corresponding to the dataset file and repository.
 
 
 There are some optional parameters, such as the number of CPUs to use for the calculations, the maximum number of pairs for the ensemble model, the name of the label column in the dataset file, etc. By default, the outputs of the code are stored in the current repository, but it can be configured differently with the optional parameter --outdir . The ouput files comprise a txt file with the final pair classifiers for the ensemble model, a pdf file with the roc curve and pdf files visualizing the final pair classifiers.
@@ -37,4 +37,5 @@ python3 run_mem.py -h
 
 ## References
 [1] Stout, Q.F. Isotonic Regression via Partitioning. Algorithmica 66, 93–112 (2013). https://doi.org/10.1007/s00453-012-9628-4
+
 [2] Iryna Nikolayeva, Pierre Bost, Isabelle Casademont, Veasna Duong, Fanny Koeth, Matthieu Prot, Urszula Czerwinska, Sowath Ly, Kevin Bleakley, Tineke Cantaert, Philippe Dussart, Philippe Buchy, Etienne Simon-Lorière, Anavaj Sakuntabhai, Benno Schwikowski, A Blood RNA Signature Detecting Severe Disease in Young Dengue Patients at Hospital Arrival, The Journal of Infectious Diseases, Volume 217, Issue 11, Pages 1690–1698 (2018) https://doi.org/10.1093/infdis/jiy086