Access the full text.
Sign up today, get DeepDyve free for 14 days.
Emily Kaczmarek, A. Jamzad, Tashifa Imtiaz, Jina Nanayakkara, N. Renwick, P. Mousavi (2021)
Multi-Omic Graph Transformers for Cancer Classification and InterpretationBiocomputing 2022
OiSaeng Hong, M. Kerr, G. Poling, S. Dhar (2013)
Understanding and preventing noise-induced hearing loss.Disease-a-month : DM, 59 4
Latifa AlKaabi, Lina Ahmed, Maryam Attiyah, M. Abdel-Rahman (2020)
Predicting hypertension using machine learning: Findings from Qatar Biobank StudyPLoS ONE, 15
R. Davis, Peter Kozel, L. Erway (2003)
Genetic influences in individual susceptibility to noise: a review.Noise & health, 5 20
M. Vlaming, R. Mackinnon, Marije Jansen, D. Moore (2014)
Automated Screening for High-Frequency Hearing LossEar and Hearing, 35
Hong Han, Xiaoling Guo, Hua Yu (2016)
Variable selection using Mean Decrease Accuracy and Mean Decrease Gini based on Random Forest2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS)
Yuxin Zhang, Fengwu Chen, Aizhen Yang, Xiaoying Wang, Yue Han, Depei Wu, Yi Wu, Jingyu Zhang (2021)
The disulfide bond Cys2724-Cys2774 in the C-terminal cystine knot domain of von Willebrand factor is critical for its dimerization and secretionThrombosis Journal, 19
Qunfeng Cai, Minal Patel, D. Coling, B. Hu (2012)
Transcriptional changes in adhesion-related genes are site-specific during noise-induced cochlear pathogenesisNeurobiology of Disease, 45
I. Nagy, M. Trexler, L. Patthy (2008)
The second von Willebrand type A domain of cochlin has high affinity for type I, type II and type IV collagensFEBS Letters, 582
K. Ngiam, Ing Khor (2019)
Big data and machine learning algorithms for health-care delivery.The Lancet. Oncology, 20 5
L. Drouet, C. Hautefort, H. Vitaux, R. Kania, J. Callebert, A. Stépanian, V. Siguret, M. Eliezer, N. Vodovar, J. Launay (2020)
Plasma Serotonin is Elevated in Adult Patients with Sudden Sensorineural Hearing LossThrombosis and Haemostasis, 120
M. Śliwińska-Kowalska, M. Pawełczyk (2013)
Contribution of genetic factors to noise-induced hearing loss: a human studies review.Mutation research, 752 1
J. Royster (2017)
Preventing Noise-Induced Hearing LossNorth Carolina Medical Journal, 78
I. Henarejos-Castillo, A. Alemán, Begoña Martínez-Montoro, F. Gracia-Aznárez, P. Sebastián-León, M. Romeu, J. Remohí, A. Patiño-García, P. Royo, G. Alkorta-Aranburu, P. Diaz-Gimeno (2021)
Machine Learning-Based Approach Highlights the Use of a Genomic Variant Profile for Precision Medicine in Ovarian FailureJournal of Personalized Medicine, 11
Huanyu Mao, Yan Chen (2021)
Noise-Induced Hearing Loss: Updates on Molecular Targets and Potential InterventionsNeural Plasticity, 2021
A. Kim, M. Chang, J. Koo, Seung-ha Oh, B. Choi (2014)
Novel TECTA Mutations Identified in Stable Sensorineural Hearing Loss and Their Clinical ImplicationsAudiology and Neurotology, 20
Sven Kosub (2016)
A note on the triangle inequality for the Jaccard distancePattern Recognit. Lett., 120
Royster JD. (2017)
113N C Med J, 78
Pankaj Mehta, Marin Bukov, Ching-Hao Wang, A. Day, C. Richardson, Charles Fisher, D. Schwab (2018)
A high-bias, low-variance introduction to Machine Learning for physicistsPhysics reports, 810
Ì. Valle, E. Giampieri, G. Simonetti, Antonella Padella, M. Manfrini, A. Ferrari, C. Papayannidis, I. Zironi, Marianna Garonzi, S. Bernardi, M. Delledonne, G. Martinelli, D. Remondini, G. Castellani (2016)
Optimized pipeline of MuTect and GATK tools to improve the detection of somatic single nucleotide polymorphisms in whole-exome sequencing dataBMC Bioinformatics, 17
Abstract Background High-throughput sequencing of genes indicating susceptibility to noise-induced hearing loss has not previously been reported. Aims/Objectives To identify and analyze genes associated with susceptibility to noise-induced hearing loss (NIHL) and characterize differences in susceptibility to hearing loss by genotype. Material and methods Pure tone audiometry tests were performed on 113 workers exposed to high-intensity noise. Whole-exome sequencing (WES) was conducted and NIHL susceptibility genes screened for training unsupervised and supervised machine learning models. Immunofluorescence staining of mouse cochlea was used to observe patterns of NIHL susceptibility gene expression. Results Participants were divided into a NIHL and a control group, according to the results of audiometry tests. Seventy-three possible NIHL susceptibility genes were input into the machine learning model. Two subgroups of NIHL could be distinguished by unsupervised machine learning and the classification was evaluated by the supervised machine learning algorithm. The VWF gene had the highest mutation frequency in the NIHL group and was expressed mainly in the spiral ligament. Conclusions and significance NIHL susceptibility genes were screened and NIHL subgroups could be distinguished. VWF may be a novel NIHL susceptibility gene.
Acta Oto-Laryngologica – Taylor & Francis
Published: May 2, 2023
Keywords: Noise-induced hearing loss; whole-exome sequencing; machine learning; prediction model
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.