ArticleAmerican journal of human genetics2017
DOMINO: Using Machine Learning to Predict Genes Associated with Dominant Disorders.
Article in American journal of human genetics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 81 papers, 1 of them a synthesis that pooled it.
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Who cites it
81 citing papers in PubMed, 1 synthesis or guideline pooled it, 135 citations in OpenAlex.
- A systematic review and standardized clinical validity assessment of male infertility genes.Human reproduction (Oxford, England) · 2019Pooled it
- Transcriptome-directed analysis for Mendelian disease diagnosis overcomes limitations of conventional genomic testing.The Journal of clinical investigation · 2021Trial
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- A de novo Loss-of-function Variant in RAPGEF6 Supports its Role in Neuropsychiatric Disorders.Journal of molecular neuroscience : MN · 2026Article
- A De NovoInternational journal of molecular sciences · 2026Article
- Accurate and cost-effective workflow integrating trio pooled-WES for novel gene discovery in neurodevelopmental disorders.European journal of human genetics : EJHG · 2026Article
- Genetic and Epigenetic Contributors to Cleft Laterality: Evidence from Monozygotic Mirror Twins and Replication Cohorts.medRxiv : the preprint server for health sciences · 2026Article
- Rare heterozygous de novo variants in RAPGEF2 are associated with a neurodevelopmental disorder.Genetics in medicine : official journal of the American College of Medical Genetics · 2026Article
- Areas of research priorities in epilepsy: A position paper of the European Reference Network for Rare and Complex Epilepsies, EpiCARE.Epilepsia open · 2026Article
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- Expanding the Genetic Spectrum of Non-Syndromic Cleft Lip and Palate Through Whole-Exome Sequencing.International journal of molecular sciences · 2025Article
- Familial Molecular Burden in Autism Spectrum Disorder: A Next-Generation Sequencing Study of Polish Affected Families.International journal of molecular sciences · 2025Article
- Analyte Importance Analysis in Machine Learning-Based Detection of Wrong-Blood-in-Tube Errors Using Complete Blood Count Data.Journal of personalized medicine · 2025Article
- Heterozygous variants ineLife · 2025Article
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- An integrative scoring approach for prioritization of rare autism spectrum disorder candidate variants from whole exome sequencing data.Scientific reports · 2025Article
- Genetic evidence informs the direction of therapeutic modulation in drug development.npj drug discovery · 2025Article
- Exploring the Role ofGenes · 2024Article
21 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In contrast to recessive conditions with biallelic inheritance, identification of dominant (monoallelic) mutations for Mendelian disorders is more difficult, because of the abundance of benign heterozygous variants that act as massive background noise (typically, in a 400:1 excess ratio). To reduce this overflow of false positives in next-generation sequencing (NGS) screens, we developed DOMINO, a tool assessing the likelihood for a gene to harbor dominant changes. Unlike commonly-used predictors of pathogenicity, DOMINO takes into consideration features that are the properties of genes, rather than of variants. It uses a machine-learning approach to extract discriminant information from a broad array of features (N = 432), including: genomic data, intra-, and interspecies conservation, gene expression, protein-protein interactions, protein structure, etc. DOMINO's iterative architecture includes a training process on 985 genes with well-established inheritance patterns for Mendelian conditions, and repeated cross-validation that optimizes its discriminant power. When validated on 99 newly-discovered genes with pathogenic mutations, the algorithm displays an excellent final performance, with an area under the curve (AUC) of 0.92. Furthermore, unsupervised analysis by DOMINO of real sets of NGS data from individuals with intellectual disability or epilepsy correctly recognizes known genes and predicts 9 new candidates, with very high confidence. In summary, DOMINO is a robust and reliable tool that can infer dominance of candidate genes with high sensitivity and specificity, making it a useful complement to any NGS pipeline dealing with the analysis of the morbid human genome.
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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.