ArticleNature communications2018
A semi-supervised approach for predicting cell-type specific functional consequences of non-coding variation using MPRAs.
Article in Nature communications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed.
- BTS: a scalable Bayesian Tissue Score for prioritizing GWAS variants and their functional contexts across >1000s of omics datasets.Bioinformatics (Oxford, England) · 2025Article
- ChromActivity: integrative epigenomic and functional characterization assay based annotation of regulatory activity across diverse human cell types.Genome biology · 2025Article
- Disease-specific prioritization of non-coding GWAS variants based on chromatin accessibility.HGG advances · 2024Article
- In silico generation and augmentation of regulatory variants from massively parallel reporter assay using conditional variational autoencoder.bioRxiv : the preprint server for biology · 2024Article
- sscNOVA: a semi-supervised convolutional neural network for predicting functional regulatory variants in autoimmune diseases.Frontiers in immunology · 2024Article
- Optimized high-throughput screening of non-coding variants identified from genome-wide association studies.Nucleic acids research · 2023Article
- Challenges Related to the Use of Next-Generation Sequencing for the Optimization of Drug Therapy.Handbook of experimental pharmacology · 2023Article
- Prediction accuracy of regulatory elements from sequence varies by functional sequencing technique.Frontiers in cellular and infection microbiology · 2023Article
- Deep learning-assisted genome-wide characterization of massively parallel reporter assays.Nucleic acids research · 2022Article
- Scalable approaches for functional analyses of whole-genome sequencing non-coding variants.Human molecular genetics · 2022Review
- TVAR: assessing tissue-specific functional effects of non-coding variants with deep learning.Bioinformatics (Oxford, England) · 2022Article
- Exploiting deep transfer learning for the prediction of functional non-coding variants using genomic sequence.Bioinformatics (Oxford, England) · 2022Article
- A multi-dimensional integrative scoring framework for predicting functional variants in the human genome.American journal of human genetics · 2022Article
- Prioritization of regulatory variants with tissue-specific function in the non-coding regions of human genome.Nucleic acids research · 2022Article
- VannoPortal: multiscale functional annotation of human genetic variants for interrogating molecular mechanism of traits and diseases.Nucleic acids research · 2022Article
- Powerful gene-based testing by integrating long-range chromatin interactions and knockoff genotypes.Proceedings of the National Academy of Sciences of the United States of America · 2021Article
- WEVar: a novel statistical learning framework for predicting noncoding regulatory variants.Briefings in bioinformatics · 2021Article
- A semi-supervised model to predict regulatory effects of genetic variants at single nucleotide resolution using massively parallel reporter assays.Bioinformatics (Oxford, England) · 2021Article
- Improving glomerular filtration rate estimation by semi-supervised learning: a development and external validation study.International urology and nephrology · 2021Article
- Identification of putative causal loci in whole-genome sequencing data via knockoff statistics.Nature communications · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Predicting the functional consequences of genetic variants in non-coding regions is a challenging problem. We propose here a semi-supervised approach, GenoNet, to jointly utilize experimentally confirmed regulatory variants (labeled variants), millions of unlabeled variants genome-wide, and more than a thousand cell/tissue type specific epigenetic annotations to predict functional consequences of non-coding variants. Through the application to several experimental datasets, we demonstrate that the proposed method significantly improves prediction accuracy compared to existing functional prediction methods at the tissue/cell type level, but especially so at the organism level. Importantly, we illustrate how the GenoNet scores can help in fine-mapping at GWAS loci, and in the discovery of disease associated genes in sequencing studies. As more comprehensive lists of experimentally validated variants become available over the next few years, semi-supervised methods like GenoNet can be used to provide increasingly accurate functional predictions for variants genome-wide and across a variety of cell/tissue types.
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