Abstract
Personalized medicine promises to revolutionize healthcare in the coming years. However significant challenges remain, namely in regard to integrating the vast amount of biomedical knowledge generated in the last few years. Here we describe an approach that uses Knowledge Graph Embedding (KGE) methods on a biomedical Knowledge Graph as a path to reasoning over the wealth of information stored in publicly accessible databases. We use curated databases such as Ensembl, DisGeNET and Gene Ontology as data sources to build a Knowledge Graph containing relationships between genes, diseases and other biological entities and explore the potential of KGE methods to derive medically relevant insights from this KG. To showcase the method’s usefulness we describe two use cases: a) prediction of gene-disease associations and b) clustering of disease embeddings. We show that the top gene-disease associations predicted by this approach can be confirmed in external databases or have already been identified in the literature. An analysis of clusters of diseases, with a focus on Autism Spectrum Disorder (ASD), affords novel insights into the biology of this paradigmatic complex disorder and the overlap of its genetic background with other diseases.
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Acknowledgements
The authors would like to acknowledge the support by the UID/MULTI/04046/2019 centre grant from FCT, Portugal (to BioISI), and the MedPerSyst project (POCI-01-0145-FEDER-016428-PAC) “Redes sinapticas e abordagens compreensivas de medicina personalizada em doenças neurocomportamentais ao longo da vida” (SAICTPAC/0010/2015). This work used the European Grid Infrastructure (EGI) with the support of NCG-INGRID-PT/INCD (Portugal). This work was produced with the support of INCD funded by FCT and FEDER under the project 01/SAICT/2016 n\(^{\circ }\) 022153.
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Vilela, J. et al. (2021). Biomedical Knowledge Graph Embeddings for Personalized Medicine. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science(), vol 12981. Springer, Cham. https://doi.org/10.1007/978-3-030-86230-5_46
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