ArticleHuman mutation2019
eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics.
Article in Human mutation, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- aiDIVA - hybrid AI for rare disease diagnostics using evidence-based, machine learning and language models.NPJ genomic medicine · 2026Article
- Current perspectives and challenges of using artificial intelligence in immunodeficiencies.The Journal of allergy and clinical immunology · 2025Review
- Comparison of machine learning models for mucopolysaccharidosis early diagnosis using UAE medical records.Scientific reports · 2025Article
- Review
- Rare disease genomics and precision medicine.Genomics & informatics · 2024Review
- Tissue-aware interpretation of genetic variants advances the etiology of rare diseases.Molecular systems biology · 2024Article
- Genomes in clinical care.NPJ genomic medicine · 2024Review
- Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes.Nature communications · 2024Article
- Artificial intelligence and database for NGS-based diagnosis in rare disease.Frontiers in genetics · 2023Review
- Phenotype-aware prioritisation of rare Mendelian disease variants.Trends in genetics : TIG · 2022Review
- Evaluation of phenotype-driven gene prioritization methods for Mendelian diseases.Briefings in bioinformatics · 2022Article
- Knowledge structure and emerging trends in the application of deep learning in genetics research: A bibliometric analysis [2000-2021].Frontiers in genetics · 2022Article
- VPMBench: a test bench for variant prioritization methods.BMC bioinformatics · 2021Article
- A domain damage index to prioritizing the pathogenicity of missense variants.Human mutation · 2021Article
- Powerful use of automated prioritization of candidate variants in genetic hearing loss with extreme etiologic heterogeneity.Scientific reports · 2021Article
- The IL-2RG R328X nonsense mutation allows partial STAT-5 phosphorylation and defines a critical region involved in the leaky-SCID phenotype.Clinical and experimental immunology · 2020Article
- Review
- eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics.Human mutation · 2019Article
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
Mendelian diseases have shown to be an and efficient model for connecting genotypes to phenotypes and for elucidating the function of genes. Whole-exome sequencing (WES) accelerated the study of rare Mendelian diseases in families, allowing for directly pinpointing rare causal mutations in genic regions without the need for linkage analysis. However, the low diagnostic rates of 20-30% reported for multiple WES disease studies point to the need for improved variant pathogenicity classification and causal variant prioritization methods. Here, we present the exome Disease Variant Analysis (eDiVA; http://ediva.crg.eu), an automated computational framework for identification of causal genetic variants (coding/splicing single-nucleotide variants and small insertions and deletions) for rare diseases using WES of families or parent-child trios. eDiVA combines next-generation sequencing data analysis, comprehensive functional annotation, and causal variant prioritization optimized for familial genetic disease studies. eDiVA features a machine learning-based variant pathogenicity predictor combining various genomic and evolutionary signatures. Clinical information, such as disease phenotype or mode of inheritance, is incorporated to improve the precision of the prioritization algorithm. Benchmarking against state-of-the-art competitors demonstrates that eDiVA consistently performed as a good or better than existing approach in terms of detection rate and precision. Moreover, we applied eDiVA to several familial disease cases to demonstrate its clinical applicability.
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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 OpenQuestion graph.