ArticleScientific reports2019
Using Machine Learning to Predict Sensorineural Hearing Loss Based on Perilymph Micro RNA Expression Profile.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 33 citations in OpenAlex.
- Pooled it
- Hearing Recovery Prediction for Patients with Chronic Otitis Media Who Underwent Canal-Wall-Down Mastoidectomy.Journal of clinical medicine · 2024Article
- Extracellular vesicles for developing targeted hearing loss therapy.Journal of controlled release : official journal of the Controlled Release Society · 2024Review
- Combination of static and dynamic neural imaging features to distinguish sensorineural hearing loss: a machine learning study.Frontiers in neuroscience · 2024Article
- In Silico Localization of Perilymph Proteins Enriched in Meńier̀e Disease Using Mammalian Cochlear Single-cell Transcriptomics.Otology & neurotology open · 2023Article
- NcRNA: key and potential in hearing loss.Frontiers in neuroscience · 2023Review
- Extracellular Biomarkers of Inner Ear Disease and Their Potential for Point-of-Care Diagnostics.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2022Review
- Precision machine learning to understand micro-RNA regulation in neurodegenerative diseases.Frontiers in molecular neuroscience · 2022Review
- Article
- Isolation of sensory hair cell specific exosomes in human perilymph.Neuroscience letters · 2021Observational
- Personalized Proteomics for Precision Diagnostics in Hearing Loss: Disease-Specific Analysis of Human Perilymph by Mass Spectrometry.ACS omega · 2021Article
- The Emerging Role of Long Non-Coding RNAs and MicroRNAs in Neurodegenerative Diseases: A Perspective of Machine Learning.Biomolecules · 2021Review
- MicroRNA Profiling as a Methodology to Diagnose Ménière's Disease: Potential Application of Machine Learning.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2021Article
- Distinct MicroRNA Profiles in the Perilymph and Serum of Patients With Menière's Disease.Frontiers in neurology · 2021Article
- Establishment of a prediction tool for ocular trauma patients with machine learning algorithm.International journal of ophthalmology · 2021Article
- Recent Advances in the Application of Artificial Intelligence in Otorhinolaryngology-Head and Neck Surgery.Clinical and experimental otorhinolaryngology · 2020Review
- Objective Assessment System for Hearing Prediction Based on Stimulus-Frequency Otoacoustic Emissions.Trends in hearingArticle
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Hearing loss (HL) is the most common neurodegenerative disease worldwide. Despite its prevalence, clinical testing does not yield a cell or molecular based identification of the underlying etiology of hearing loss making development of pharmacological or molecular treatments challenging. A key to improving the diagnosis of inner ear disorders is the development of reliable biomarkers for different inner ear diseases. Analysis of microRNAs (miRNA) in tissue and body fluid samples has gained significant momentum as a diagnostic tool for a wide variety of diseases. In previous work, we have shown that miRNA profiling in inner ear perilymph is feasible and may demonstrate distinctive miRNA expression profiles unique to different diseases. A first step in developing miRNAs as biomarkers for inner ear disease is linking patterns of miRNA expression in perilymph to clinically available metrics. Using machine learning (ML), we demonstrate we can build disease specific algorithms that predict the presence of sensorineural hearing loss using only miRNA expression profiles. This methodology not only affords the opportunity to understand what is occurring on a molecular level, but may offer an approach to diagnosing patients with active inner ear disease.
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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.