This repository provides an implementation of the DTiGEMSplus tool, a network-based method for computational Drug-Target Interaction prediction using graph embedding, graph mining, and similarity-based techniques
(Published in Journal of Cheminformatics 29 June 2020):
DTiGEMS+: a network-based method for computational Drug-Target Interaction prediction using graph embedding, graph mining, and similarity-based techniques
Everything about the source code usage is explained in ReadME.md file inside the folder DTiGEMS+ https://github.com/MahaThafar/Drug-Target-Interaction-Prediciton-Method/tree/master/DTiGEMS%2B
About the folder (DTIs_node2vec):
python DTIs_Main.py
python DTIs_Main.py --dimension 32 --p 0.25 --q 2 --walk-length 30
When you run the code the AUPR result could be a little bit different than the other code (DTIs_Main_ic.py) because of randomness in node2vec when generates the embedding
(all details to run the code as well as required parameters are provided with node2vec source code)
https://github.com/aditya-grover/node2vec
Thafar, M.A., Olayan, R.S., Ashoor, H. et al. DTiGEMS+: drug–target interaction prediction using graph embedding, graph mining, and similarity-based techniques. J Cheminform 12, 44 (2020). https://doi.org/10.1186/s13321-020-00447-2