ArticleEuropean journal of human genetics : EJHG2021
Prioritization of disease genes from GWAS using ensemble-based positive-unlabeled learning.
Article in European journal of human genetics : EJHG, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Genome-scale perturbation signatures from primary human CD4bioRxiv : the preprint server for biology · 2026Article
- CLinNET: An Interpretable and Uncertainty-Aware Deep Learning Framework for Multi-Modal Clinical Genomics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Concordance between male- and female-specific GWAS results helps define underlying genetic architecture of complex traits.Nature communications · 2025Article
- Big data and artificial intelligence-aided crop breeding: Progress and prospects.Journal of integrative plant biology · 2025Review
- Tissue specific tumor-gene link prediction through sampling based GNN using a heterogeneous network.Medical & biological engineering & computing · 2024Article
- Complex trait susceptibilities and population diversity in a sample of 4,145 Russians.Nature communications · 2024Article
- Imperfect gold standard gene sets yield inaccurate evaluation of causal gene identification methods.Communications biology · 2024Review
- Positive-unlabeled learning to infer protection status and identify correlates in vaccine efficacy field trials.iScience · 2024Article
- Gene, cell type, and drug prioritization analysis suggest genetic basis for the utility of diuretics in treating Alzheimer disease.HGG advances · 2023Article
- E-GWAS: an ensemble-like GWAS strategy that provides effective control over false positive rates without decreasing true positives.Genetics, selection, evolution : GSE · 2023Article
- MultiCens: Multilayer network centrality measures to uncover molecular mediators of tissue-tissue communication.PLoS computational biology · 2023Article
- Detecting biased validation of predictive models in the positive-unlabeled setting: disease gene prioritization case study.Bioinformatics advances · 2023Article
- Improving Genomic Prediction with Machine Learning Incorporating TPE for Hyperparameters Optimization.Biology · 2022Article
- A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning.BMC bioinformatics · 2022Article
- GWAS of depression in 4,520 individuals from the Russian population highlights the role ofFrontiers in genetics · 2022Article
- Scientific Basics of Personalized Medicine: Realities and Opportunities.Herald of the Russian Academy of Sciences · 2022Article
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3 authors.
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No grant is acknowledged in the PubMed record.
Abstract
A primary challenge in understanding disease biology from genome-wide association studies (GWAS) arises from the inability to directly implicate causal genes from association data. Integration of multiple-omics data sources potentially provides important functional links between associated variants and candidate genes. Machine-learning is well-positioned to take advantage of a variety of such data and provide a solution for the prioritization of disease genes. Yet, classical positive-negative classifiers impose strong limitations on the gene prioritization procedure, such as a lack of reliable non-causal genes for training. Here, we developed a novel gene prioritization tool-Gene Prioritizer (GPrior). It is an ensemble of five positive-unlabeled bagging classifiers (Logistic Regression, Support Vector Machine, Random Forest, Decision Tree, Adaptive Boosting), that treats all genes of unknown relevance as an unlabeled set. GPrior selects an optimal composition of algorithms to tune the model for each specific phenotype. Altogether, GPrior fills an important niche of methods for GWAS data post-processing, significantly improving the ability to pinpoint disease genes compared to existing solutions.
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