This repository has been created to present the recent work done on drug repositioning thanks to Non-Negative Matrix Factorization [1,2].
The jupyter notebook results.ipynb presents these results.
This repository contains all data, scripts and results related to our recent work. In particular, you will find:
If you want to run these files, you may need to install the following packages:
sklearn, matplotlib, tqdm, scipy, numpy, pandas, seaborn, csv, cs, spherecluster
[1] Dissez, G. and Ceddia G., Pinoli, P. and Ceri, S. and Masseroli, M. (2019). Drug repositioning predictions by non-negative matrix tri-factorization of integrated association data. Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics, 25-33.
[2] Ceddia, G. and Pinoli, P. and Ceri, S. and Masseroli, M. (2020). Matrix Factorization-based Technique for Drug Repurposing Predictions. IEEE Journal of Biomedical and Health Informatics, 24(11), 3162-3172.