Fig. 1

Computational analysis pipeline for drug target prioritization. (Step 1) Our computational framework employed genome-wide PINs and information of drug targets obtained from public domain databases. (Step 2) The framework is based on a deep autoencoder to extract low-dimensional latent features from high-dimensional PIN. (Step 3) By using features from step 2 and a target gene list for a specific disease, we generated 100 datasets to train the 100 classifier models. By using the 100 datasets and the state-of-the-art machine learning techniques (SMOTE and Xgboost), we build 100 classifier models to infer potential drug targets. (Step 4) We applied the classifier models to all unknown drug-target genes in the PIN to prioritize potential drug target genes