Biomedical Network Link Prediction using Neural Network Graph Embedding
Sumit Kumar, Raj Ratn Pranesh, Ambesh Shekhar · 2020
In this paper, we aim at Graph embedding learning for automatic grasping of low-dimensional node representation on biomedical networks. The purpose is to use different neural Graph embedding methods for conducting analysis on 3 major biomedical link prediction tasks: drug-disease association (DDA) prediction, drug-drug interaction (DDI) classification, and protein-protein interaction (PPI) classification. We observe that graph embedding method achieve a promising result without the use of any biological features.