ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2022
A knowledge graph-based disease-gene prediction system using multi-relational graph convolution networks.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
8 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A Systematic Review of the Application of Graph Neural Networks to Extract Candidate Genes and Biological Associations.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2025Pooled it
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
- KG2ML: integrating knowledge graphs and positive unlabeled learning for identifying disease-associated genes.Frontiers in bioinformatics · 2025Article
- Article
- Predicting gene disease associations with knowledge graph embeddings for diseases with curtailed information.NAR genomics and bioinformatics · 2024Article
- Elucidating the semantics-topology trade-off for knowledge inference-based pharmacological discovery.Journal of biomedical semantics · 2024Article
- KDGene: knowledge graph completion for disease gene prediction using interactional tensor decomposition.Briefings in bioinformatics · 2024Article
- Repurposing ketamine to treat cocaine use disorder: integration of artificial intelligence-based prediction, expert evaluation, clinical corroboration and mechanism of action analyses.Addiction (Abingdon, England) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Identifying disease-gene associations is important for understanding molecule mechanisms of diseases, finding diagnostic markers and therapeutic targets. Many computational methods have been proposed to predict disease related genes by integrating different biological databases into heterogeneous networks. However, it remains a challenging task to leverage heterogeneous topological and semantic information from multi-source biological data to enhance disease-gene prediction. In this study, we propose a knowledge graph-based disease-gene prediction system (GenePredict-KG) by modeling semantic relations extracted from various genotypic and phenotypic databases. We first constructed a knowledge graph that comprised 2,292,609 associations between 73,358 entities for 14 types of phenotypic and genotypic relations and 7 entity types. We developed a knowledge graph embedding model to learn low-dimensional representations of entities and relations, and utilized these embeddings to infer new disease-gene interactions. We compared GenePredict-KG with several state-of-the-art models using multiple evaluation metrics. GenePredict-KG achieved high performances [AUROC (the area under receiver operating characteristic) = 0.978, AUPR (the area under precision-recall) = 0.343 and MRR (the mean reciprocal rank) = 0.244], outperforming other state-of-art methods.
Indexed as
Identifiers
37128437PMC10148306What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.