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.
Mining On Alzheimer's Diseases Related Knowledge Graph to Identity Potential AD-related Semantic Triples for Drug Repurposing
University of Texas Health Science Center at Houston（休斯顿德克萨斯大学健康科学中心）；University of Minnesota（明尼苏达大学）；University of Pennsylvania（宾夕法尼亚大学）；University of Maryland School of Medicine（马里兰大学医学院）
RareAgent: Self-Evolving Reasoning for Drug Repurposing in Rare Diseases
Information Hub, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）信息中心）；College of Computer Science, Nankai University（南开大学计算机学院）；Department of Computer Science, University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机科学系）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
BioGraphFusion: Graph Knowledge Embedding for Biological Completion and Reasoning
College of Computer Science；Technology , Zhejiang University of Technology , Hangzhou , Zhejiang , 310023 , China；Key Laboratory of Endocrine Glucose \& Lipids Metabolism , Department of Endocrinology, Shandong Provincial Hospital Affiliated to Shandong First Medical University , Jinan , Shandong , China
GLaDiGAtor: Language-Model-Augmented Multi-Relation Graph Learning for Predicting Disease-Gene Associations
Biological Data Science Lab, Dept. of Computer Engineering, Hacettepe University（生物数据科学实验室，计算机工程系，哈切泰佩大学）；Dept. of Bioinformatics, Graduate School of Health Sciences, Hacettepe University（生物信息学系，健康科学研究生院，哈切泰佩大学）；Dept. of Health Informatics, Institute of Informatics, Hacettepe University（健康信息学系，信息学院，哈切泰佩大学）
