Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Interaction Network Analysis Using Semantic Similarity Based on Translation Embeddings

: Bajwa, Awais Manzoor; Collarana, Diego; Vidal, Maria-Esther

Fulltext ()

Acosta, M.:
Semantic Systems. The Power of AI and Knowledge Graphs. Proceedings : 15th International Conference, SEMANTiCS 2019, Karlsruhe, Germany, September 9-12, 2019
Cham: Springer Nature, 2019 (Lecture Notes in Computer Science 11702)
ISBN: 978-3-030-33219-8 (Print)
ISBN: 978-3-030-33220-4 (Online)
International Conference on Semantic Systems (SEMANTiCS) <15, 2019, Karlsruhe>
Conference Paper, Electronic Publication
Fraunhofer IAIS ()
Knowledge Graphs; embeddings; Similarity Function

Biomedical knowledge graphs such as STITCH, SIDER, and Drugbank provide the basis for the discovery of associations between biomedical entities, e.g., interactions between drugs and targets. Link prediction is a paramount task and represents a building block for supporting knowledge discovery. Although several approaches have been proposed for effectively predicting links, the role of semantics has not been studied in depth. In this work, we tackle the problem of discovering interactions between drugs and targets, and propose SimTransE, a machine learning-based approach that solves this problem effectively. SimTransE relies on translating embeddings to model drug-target interactions and values of similarity across them. Grounded on the vectorial representation of drug-target interactions, SimTransE is able to discover novel drug-target interactions. We empirically study SimTransE using state-of-the-art benchmarks and approaches. Experimental results suggest that SimTransE is competitive with the state of the art, representing, thus, an effective alternative for knowledge discovery in the biomedical domain.