ArticleBMC medical genomics2018
Phenotype-driven gene prioritization for rare diseases using graph convolution on heterogeneous networks.
Article in BMC medical genomics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline 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
- NovelHTI: An Interpretable Pathway-Enhanced Framework for De Novo Target Prediction of Medicinal Herbs via Cross-Scale Heterogeneous Information Fusion.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Article
- A Hypergraph powered approach to Phenotype-driven Gene Prioritization and Rare Disease Prediction.Scientific reports · 2025Article
- Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases.NPJ digital medicine · 2025Article
- A phenotype-based AI pipeline outperforms human experts in differentially diagnosing rare diseases using EHRs.NPJ digital medicine · 2025Article
- Leveraging Social Determinants of Health in Alzheimer's Research Using LLM-Augmented Literature Mining and Knowledge Graphs.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2025Article
- Survey and improvement strategies for gene prioritization with large language models.Bioinformatics advances · 2025Article
- An end-to-end method for predicting compound-protein interactions based on simplified homogeneous graph convolutional network and pre-trained language model.Journal of cheminformatics · 2024Article
- ReHoGCNES-MDA: prediction of miRNA-disease associations using homogenous graph convolutional networks based on regular graph with random edge sampler.Briefings in bioinformatics · 2024Article
- Machine learning in rare disease.Nature methods · 2023Review
- A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning.BMC bioinformatics · 2022Article
- New Developments and Possibilities in Reanalysis and Reinterpretation of Whole Exome Sequencing Datasets for Unsolved Rare Diseases Using Machine Learning Approaches.International journal of molecular sciences · 2022Review
- Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases.Briefings in bioinformatics · 2022Article
- Predicting non-small cell lung cancer-related genes by a new network-based machine learning method.Frontiers in oncology · 2022Article
- Using expression quantitative trait loci data and graph-embedded neural networks to uncover genotype-phenotype interactions.Frontiers in genetics · 2022Article
- Identifying potential association on gene-disease network via dual hypergraph regularized least squares.BMC genomics · 2021Article
- NOGEA: A Network-oriented Gene Entropy Approach for Dissecting Disease Comorbidity and Drug Repositioning.Genomics, proteomics & bioinformatics · 2021Article
- Graph Neural Networks and Their Current Applications in Bioinformatics.Frontiers in genetics · 2021Article
- Interpretable Clinical Genomics with a Likelihood Ratio Paradigm.American journal of human genetics · 2020Article
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6 authors.
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Abstract
backgroundOne of the major goals of genomic medicine is the identification of causal genomic variants in a patient and their relation to the observed clinical phenotypes. Prioritizing the genomic variants by considering only the genotype information usually identifies a few hundred potential variants. Narrowing it down further to find the causal disease genes and relating them to the observed clinical phenotypes remains a significant challenge, especially for rare diseases.
methodsWe propose a phenotype-driven gene prioritization approach using heterogeneous networks in the context of rare diseases. Towards this, we first built a heterogeneous network consisting of ontological associations as well as curated associations involving genes, diseases, phenotypes and pathways from multiple sources. Motivated by the recent progress in spectral graph convolutions, we developed a graph convolution based technique to infer new phenotype-gene associations from this initial set of associations. We included these inferred associations in the initial network and termed this integrated network HANRD (Heterogeneous Association Network for Rare Diseases). We validated this approach on 230 recently published rare disease clinical cases using the case phenotypes as input.
resultsWhen HANRD was queried with the case phenotypes as input, the causal genes were captured within Top-50 for more than 31% of the cases and within Top-200 for more than 56% of the cases. The results showed improved performance when compared to other state-of-the-art tools.
conclusionsIn this study, we showed that the heterogeneous network HANRD, consisting of curated, ontological and inferred associations, helped improve causal gene identification in rare diseases. HANRD allows future enhancements by supporting incorporation of new entity types and additional information sources.
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