Embedding Biomedical Ontologies by Jointly Encoding Network Structure and Textual Node Descriptors
This work addresses the challenge of integrating textual information with network structure for bioinformatics applications, but it is incremental as it builds on existing methods.
The paper tackled the problem of network embedding for biomedical ontologies by extending NODE2VEC to incorporate textual node descriptors, achieving improved link prediction results on UMLS-derived networks.
Network Embedding (NE) methods, which map network nodes to low-dimensional feature vectors, have wide applications in network analysis and bioinformatics. Many existing NE methods rely only on network structure, overlooking other information associated with the nodes, e.g., text describing the nodes. Recent attempts to combine the two sources of information only consider local network structure. We extend NODE2VEC, a well-known NE method that considers broader network structure, to also consider textual node descriptors using recurrent neural encoders. Our method is evaluated on link prediction in two networks derived from UMLS. Experimental results demonstrate the effectiveness of the proposed approach compared to previous work.