Update README.md

Author: jjanizek

README.md

@@ -4,3 +4,5 @@ Code for paper "Uncovering expression signatures of synergistic drug response in
 The "Benchmark" directory contains the scripts necessary to run the feature discovery benchmark experiments corresponding to Fig. 2 and Fig. 3 in the paper.
 
 The "AMLAnalysis" directory contains the scripts necessary to run the AML data analysis experiments corresponding to Fig. 4, Fig. 5, and Fig. 6 in the paper.
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+This software was originally designed and run on a system running Ubuntu 16.04.3 with Python 3.3.6. For neural network model training and interpretation, we used a single Nvidia GeForce GTX 980 Ti GPU, though we anticipate that other GPUs will also work. Standard python software packages used: PyTorch (1.9.0), XGBoost (1.4.2), scikit-learn (0.24.2), numpy (1.21.2), scipy (1.7.1), pandas (1.3.2), matplotlib (3.4.3), seaborn (0.11.2), networkx (2.6.2), tqdm (4.62.1). For model interpretability, we additionally used the following Python software packages available here: SHAP (0.39.0), and SAGE (0.0.4). To interface to the R language through Python, we used the rpy2 (3.4.5) library, which requires an existing R (4.1.1) installation. The following modules from the Python Standard Library were also used: pickle, random, copy, itertools.