ArticleHuman mutation2013
AudioGene: predicting hearing loss genotypes from phenotypes to guide genetic screening.
Article in Human mutation, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04331015 (Positive Predictive Value of Machine Learning Tools), which is not on this map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Positive Predictive Value of Machine Learning Tools (Audiogene v4.0) for Diagnosing DFNA9 in a Large Series of p.Pro51Ser Variant Carriers in COCH.
Who cites it
19 citing papers in PubMed.
- Identification of a novel isoform of Slc26a4 by single-cell RNA-sequencing of pendrin-expressing cells in the cochlea.Human genetics · 2026Article
- The AudioGene Translational Dashboard for Diagnosing Autosomal Dominant Nonsyndromic Hearing Loss: Phenotypic Data Visualization and Analysis Study.JMIR bioinformatics and biotechnology · 2026Article
- The natural history and genotype-phenotype correlations of TMPRSS3 hearing loss: an international, multi-center, cohort analysis.Human genetics · 2024Article
- Analysis of Serum Inflammatory Markers in Infants Under 6 Months of Age with Non-Syndromic Moderate and Severe Hearing Loss Associated with GJB2 Gene Mutations.Medical science monitor : international medical journal of experimental and clinical research · 2023Article
- The natural history of OTOF-related auditory neuropathy spectrum disorders: a multicenter study.Human genetics · 2022Article
- Differential genetic diagnoses of adult post-lingual hearing loss according to the audiogram pattern and novel candidate gene evaluation.Human genetics · 2022Article
- The genetic etiology of hearing loss in Japan revealed by the social health insurance-based genetic testing of 10K patients.Human genetics · 2022Review
- AudioGene: refining the natural history of KCNQ4, GSDME, WFS1, and COCH-associated hearing loss.Human genetics · 2022Article
- Early onset adult deafness in the Rhodesian Ridgeback dog is associated with an in-frame deletion in the EPS8L2 gene.PloS one · 2022Article
- Comprehensive molecular-genetic analysis of mid-frequency sensorineural hearing loss.Scientific reports · 2021Article
- A comparative analysis of genetic hearing loss phenotypes in European/American and Japanese populations.Human genetics · 2020Article
- Computational analysis based on audioprofiles: A new possibility for patient stratification in office-based otology.Audiology research · 2019Article
- Novel Usher syndrome pathogenic variants identified in cases with hearing and vision loss.BMC medical genetics · 2019Article
- Outlook and future of inner ear therapy.Hearing research · 2018Review
- Congenital hearing loss.Nature reviews. Disease primers · 2017Review
- Genetic Linkage Analysis of DFNB3, DFNB9 and DFNB21 Loci in GJB2 Negative Families with Autosomal Recessive Non-syndromic Hearing Loss.Iranian journal of public health · 2016Article
- Audioprofile Surfaces: The 21st Century Audiogram.The Annals of otology, rhinology, and laryngology · 2016Article
- Review
- Diagnosing and Preventing Hearing Loss in the Genomic Age.Trends in hearingReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
Abstract
Autosomal dominant nonsyndromic hearing loss (ADNSHL) is a common and often progressive sensory deficit. ADNSHL displays a high degree of genetic heterogeneity and varying rates of progression. Accurate, comprehensive, and cost-effective genetic testing facilitates genetic counseling and provides valuable prognostic information to affected individuals. In this article, we describe the algorithm underlying AudioGene, a software system employing machine-learning techniques that utilizes phenotypic information derived from audiograms to predict the genetic cause of hearing loss in persons segregating ADNSHL. Our data show that AudioGene has an accuracy of 68% in predicting the causative gene within its top three predictions, as compared with 44% for a majority classifier. We also show that AudioGene remains effective for audiograms with high levels of clinical measurement noise. We identify audiometric outliers for each genetic locus and hypothesize that outliers may reflect modifying genetic effects. As personalized genomic medicine becomes more common, AudioGene will be increasingly useful as a phenotypic filter to assess pathogenicity of variants identified by massively parallel sequencing.
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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.