Biomedical Network Link Prediction using Neural Network Graph Embedding
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 interactions (DDI) classification, and protein-protein interaction (PPI)classification. We observe that the graph embedding method achieve a promising result without the use of any biological features.
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