New Gene Embedding Learned from Biomedical Literature and Its Application in Identifying Cancer Drivers

Abstract

To investigate molecular mechanism of diseases, we need to understand how genes are functionally associated. Computational researchers have tried to capture functional relationships among genes by constructing an embedding space of genes from multiple sources of high-throughput data. However, correlations in high-throughput data does not necessarily imply functional relations. In this study, we generated gene embedding from literature by constructing semantic representation for each gene. This approach enabled us to cover genes less mentioned in literature and revealed novel functional relationships among genes. Evaluation showed that the learned gene embedding was consistent with pathway knowledge and enhanced the search for cancer driver genes. We further applied our gene embedding to identify protein complexes and functional modules from gene networks. Performance in both scenarios was significantly improved with gene embedding.