ArticleBioinformatics (Oxford, England)2021
Predicting candidate genes from phenotypes, functions and anatomical site of expression.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed.
- INDIGENA: inductive prediction of disease-gene associations using phenotype ontologies.Bioinformatics (Oxford, England) · 2026Article
- Knowledge graphs in psychiatric research: Potential applications and future perspectives.Acta psychiatrica Scandinavica · 2025Review
- Heterogeneous graph neural networks for link prediction in biomedical networks.Bioinformatics advances · 2025Review
- Learning genotype-phenotype associations from gaps in multi-species sequence alignments.Briefings in bioinformatics · 2024Article
- Prioritizing genomic variants through neuro-symbolic, knowledge-enhanced learning.Bioinformatics (Oxford, England) · 2024Article
- Enabling personalised disease diagnosis by combining a patient's time-specific gene expression profile with a biomedical knowledge base.BMC bioinformatics · 2024Article
- Clustering rare diseases within an ontology-enriched knowledge graph.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- SSLpheno: a self-supervised learning approach for gene-phenotype association prediction using protein-protein interactions and gene ontology data.Bioinformatics (Oxford, England) · 2023Article
- Integrative rare disease biomedical profile based network supporting drug repurposing or repositioning, a case study of glioblastoma.Orphanet journal of rare diseases · 2023Article
- Integrative Rare Disease Biomedical Profile based Network Supporting Drug Repurposing, a case study of Glioblastoma.Research square · 2023Article
- Clustering rare diseases within an ontology-enriched knowledge graph.bioRxiv : the preprint server for biology · 2023Article
- Bioinformatics approaches for unveiling virus-host interactions.Computational and structural biotechnology journal · 2023Review
- Leveraging genetic diversity to understand monogenic Parkinson's disease's landscape in AfrAbia.American journal of neurodegenerative disease · 2023Review
- Recent developments of sequence-based prediction of protein-protein interactions.Biophysical reviews · 2022Review
- KG-Predict: A knowledge graph computational framework for drug repurposing.Journal of biomedical informatics · 2022Article
- Contribution of model organism phenotypes to the computational identification of human disease genes.Disease models & mechanisms · 2022Article
- Artificial intelligence in cancer target identification and drug discovery.Signal transduction and targeted therapy · 2022Review
- DeepSVP: integration of genotype and phenotype for structural variant prioritization using deep learning.Bioinformatics (Oxford, England) · 2022Article
- A knowledge graph-based disease-gene prediction system using multi-relational graph convolution networks.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Current Status of Next-Generation Sequencing Approaches for Candidate Gene Discovery in Familial Parkinson´s Disease.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationOver the past years, many computational methods have been developed to incorporate information about phenotypes for disease-gene prioritization task. These methods generally compute the similarity between a patient's phenotypes and a database of gene-phenotype to find the most phenotypically similar match. The main limitation in these methods is their reliance on knowledge about phenotypes associated with particular genes, which is not complete in humans as well as in many model organisms, such as the mouse and fish. Information about functions of gene products and anatomical site of gene expression is available for more genes and can also be related to phenotypes through ontologies and machine-learning models.
resultsWe developed a novel graph-based machine-learning method for biomedical ontologies, which is able to exploit axioms in ontologies and other graph-structured data. Using our machine-learning method, we embed genes based on their associated phenotypes, functions of the gene products and anatomical location of gene expression. We then develop a machine-learning model to predict gene-disease associations based on the associations between genes and multiple biomedical ontologies, and this model significantly improves over state-of-the-art methods. Furthermore, we extend phenotype-based gene prioritization methods significantly to all genes, which are associated with phenotypes, functions or site of expression. AVAILABILITY AND IMPLEMENTATION: Software and data are available at https://github.com/bio-ontology-research-group/DL2Vec. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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 Socratic graph.