Knowledge Graph Completion based on Tensor Decomposition for Disease Gene Prediction
This work addresses the problem of identifying disease genes for biomedical researchers, offering a scalable framework that improves accuracy over existing methods, though it is incremental as it builds on tensor decomposition techniques.
The authors tackled disease gene prediction by constructing a biological knowledge graph and developing KDGene, an end-to-end model using interactional tensor decomposition, which significantly outperformed state-of-the-art algorithms and identified accurate candidate genes in a diabetes case study.
Accurate identification of disease genes has consistently been one of the keys to decoding a disease's molecular mechanism. Most current approaches focus on constructing biological networks and utilizing machine learning, especially, deep learning to identify disease genes, but ignore the complex relations between entities in the biological knowledge graph. In this paper, we construct a biological knowledge graph centered on diseases and genes, and develop an end-to-end Knowledge graph completion model for Disease Gene Prediction using interactional tensor decomposition (called KDGene). KDGene introduces an interaction module between the embeddings of entities and relations to tensor decomposition, which can effectively enhance the information interaction in biological knowledge. Experimental results show that KDGene significantly outperforms state-of-the-art algorithms. Furthermore, the comprehensive biological analysis of the case of diabetes mellitus confirms KDGene's ability for identifying new and accurate candidate genes. This work proposes a scalable knowledge graph completion framework to identify disease candidate genes, from which the results are promising to provide valuable references for further wet experiments.