ArticleGenome medicine2022
MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning.
Article in Genome medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 123 papers, 1 of them a synthesis that pooled it.
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Who cites it
123 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The difference of variation types between late-onset multiple acyl-CoA dehydrogenase deficiency patients carrying biallelic and single heterozygous variations in ETFDH: a systematic review and meta-analysis.Orphanet journal of rare diseases · 2025Pooled it
- Advancing genetic pathogenicity prediction with three-dimensional proteoform-phenotype analysis.medRxiv : the preprint server for health sciences · 2026Article
- A heritability-optimized method for functional prioritization of rare coding variants in complex traits.Nature genetics · 2026Article
- From Pathogenicity to Mechanism: A Variant Interpretation Framework for Monogenic Epilepsy.Clinical genetics · 2026Article
- From conservation scores to language models: a feature engineering perspective on machine learning for genetic variant interpretation.Briefings in bioinformatics · 2026Review
- Why variant effect predictors and multiplexed assays agree and disagree.Nature communications · 2026Article
- PubMind: literature-based genetic variant extraction and functional annotation using large language models.Nature communications · 2026Article
- SIMLINK enables accurate variant pathogenicity prediction through modeling the gene-variant-feature association structure.Bioinformatics (Oxford, England) · 2026Article
- Unveiling New Insights: ReinterpretingClinical case reports · 2026Article
- Identification and functional validation of a novel disease-causing variant in the noncoding region of NYX.Acta ophthalmologica · 2026Article
- Article
- The NeuroWES project: lessons learned from comprehensive phenotyping and genetic analysis of neurodevelopmental disorders over a decade.Human genetics · 2026Article
- Deep Learning and Experimental Validation of BRCA1-Associated Protein-1 Missense Variant Pathogenicity in Uveal Melanoma.Ophthalmology science · 2026Article
- Domain-Specific Computational, Functional and Structural Methods Enable Interpretation ofCurrent oncology (Toronto, Ont.) · 2026Article
- Article
- UK Biobank-Based Genetic and Proteomic Network Insights into Metabolic Dysfunction-Associated Steatotic Liver Disease Pathogenesis.International journal of molecular sciences · 2026Article
- MetaXVP: an interpretable machine learning framework for deep insight into variant pathogenicity and VUS classification.Scientific reports · 2026Article
- CDsyn: A comprehensive database for deleterious human synonymous variation prediction.iScience · 2026Article
- AProceedings of the National Academy of Sciences of the United States of America · 2026Article
- Novel Mutations in KCNJ10 Gene Associated With SeSAME Syndrome: Rare Disorder With Possible Common Mutation.Molecular genetics & genomic medicine · 2026Article
63 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
Multiple computational approaches have been developed to improve our understanding of genetic variants. However, their ability to identify rare pathogenic variants from rare benign ones is still lacking. Using context annotations and deep learning methods, we present pathogenicity prediction models, MetaRNN and MetaRNN-indel, to help identify and prioritize rare nonsynonymous single nucleotide variants (nsSNVs) and non-frameshift insertion/deletions (nfINDELs). We use independent test sets to demonstrate that these new models outperform state-of-the-art competitors and achieve a more interpretable score distribution. Importantly, prediction scores from both models are comparable, enabling easy adoption of integrated genotype-phenotype association analysis methods. All pre-computed nsSNV scores are available at http://www.liulab.science/MetaRNN . The stand-alone program is also available at https://github.com/Chang-Li2019/MetaRNN .
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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.