Machine Learning Model Identifies Genomic Variation in Noise-Induced Hearing Loss via Whole-exome Sequencing

Abstract

Abstract Noise-induced hearing loss (NIHL) is related to noise exposure and whether humans suffered from NIHL is also related to different sensitivity to noise. Whole exome sequencing (WES) can detect the genetic mutations of NIHL population so as to find the susceptibility of different people from the genetic level. WES can identify contributing genes, not only find NIHL susceptibility genes, but also divide patient cohort into different subtypes. We conducted a case-control (n = 78, n = 35, respectively) study to find the susceptibility genes of NIHL through WES and stratified the cases into subtypes, so as to achieve the purpose of precision medicine. 73 gene variants were used to train the unsupervised machine learning model which separated the case from the control group and divide the population into two subtypes. Supervised machine learning methods compared the above constructed subgroup classification models. This is the first study to link NIHL with gene variants and has produced a new potential classification method that can be applied to precision medicine in people’s different sensitivity to noise. These observations deserve further study on independent and larger clinical samples in order to provide new information for future research on the susceptibility of NIHL.