Knowledge Graph Completion based on Tensor Decomposition for Disease Gene Prediction

TL;DR

This paper presents KDGene, a novel tensor decomposition-based model for disease gene prediction that leverages biological knowledge graphs to improve accuracy over existing methods.

Contribution

The paper introduces KDGene, an end-to-end knowledge graph completion model with an interaction module, enhancing biological information interaction for disease gene prediction.

Findings

KDGene outperforms state-of-the-art algorithms in experiments.

The model accurately predicts candidate genes for diseases like diabetes mellitus.

Biological analysis confirms KDGene's effectiveness in identifying new disease-related genes.

Abstract

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.