ArticleNucleic acids research2021
DGLinker: flexible knowledge-graph prediction of disease-gene associations.
Article in Nucleic acids research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Knowledge-Driven Mechanistic Enrichment of the Preeclampsia Ignorome.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2023Pooled it
- Concurrent Alterations in Selected mRNAs and miR-30 Family Members in Canine Osteoarthritic Joint Tissues.Biomedicines · 2026Article
- Context-Aware Multilevel Classification of Semantic Relations in Drug-Adverse Drug Reaction (ADR) Networks-Predicting Drug-Induced Liver Injury (DILI) as a Case Study.Chemical research in toxicology · 2026Article
- The Data Distillery: A Graph Framework for Semantic Integration and Querying of Biomedical Data.bioRxiv : the preprint server for biology · 2025Article
- Single-cell data combined with phenotypes improves variant interpretation.BMC genomics · 2025Article
- An integrated AI knowledge graph framework of bacterial enzymology and metabolism.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
- PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants.Nature communications · 2023Article
- Enrichr-KG: bridging enrichment analysis across multiple libraries.Nucleic acids research · 2023Article
- KG-Hub-building and exchanging biological knowledge graphs.Bioinformatics (Oxford, England) · 2023Article
- Article
- The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design.Computational and structural biotechnology journal · 2022Review
- Artificial Intelligence, Healthcare, Clinical Genomics, and Pharmacogenomics Approaches in Precision Medicine.Frontiers in genetics · 2022Review
- Advances in the genetic classification of amyotrophic lateral sclerosis.Current opinion in neurology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
As a result of the advent of high-throughput technologies, there has been rapid progress in our understanding of the genetics underlying biological processes. However, despite such advances, the genetic landscape of human diseases has only marginally been disclosed. Exploiting the present availability of large amounts of biological and phenotypic data, we can use our current understanding of disease genetics to train machine learning models to predict novel genetic factors associated with the disease. To this end, we developed DGLinker, a webserver for the prediction of novel candidate genes for human diseases given a set of known disease genes. DGLinker has a user-friendly interface that allows non-expert users to exploit biomedical information from a wide range of biological and phenotypic databases, and/or to upload their own data, to generate a knowledge-graph and use machine learning to predict new disease-associated genes. The webserver includes tools to explore and interpret the results and generates publication-ready figures. DGLinker is available at https://dglinker.rosalind.kcl.ac.uk. The webserver is free and open to all users without the need for registration.
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What 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.