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Prediction of the Exposure to 1763MHz Radiofrequency Radiation Based on Gene Expression Patterns  

Lee, Min-Su (Biointelligence Laboratory, School of Computer Science and Engineering)
Huang, Tai-Qin (ILCHUN Genomic Medicine Institute, MRC and Department of Biochemistry and Molecular Biology, College of Medicine, Seoul National University)
Seo, Jeong-Sun (ILCHUN Genomic Medicine Institute, MRC and Department of Biochemistry and Molecular Biology, College of Medicine, Seoul National University)
Park, Woong-Yang (ILCHUN Genomic Medicine Institute, MRC and Department of Biochemistry and Molecular Biology, College of Medicine, Seoul National University)
Publication Information
Genomics & Informatics / v.5, no.3, 2007 , pp. 102-106 More about this Journal
Abstract
Radiofrequency (RF) radiation at the frequency of mobile phones has been not reported to induce cellular responses in in vitro and in vivo models. We exposed HEI-OC1, conditionally-immortalized mouse auditory cells, to RF radiation to characterize cellular responses to 1763 MHz RF radiation. While we could not detect any differences upon RF exposure, whole-genome expression profiling might provide the most sensitive method to find the molecular responses to RF radiation. HEI-OC1 cells were exposed to 1763 MHz RF radiation at an average specific absorption rate (SAR) of 20 W/kg for 24 hr and harvested after 5 hr of recovery (R5), alongside sham-exposed samples (S5). From the whole-genome profiles of mouse neurons, we selected 9 differentially-expressed genes between the S5 and R5 groups using information gain-based recursive feature elimination procedure. Based on support vector machine (SVM), we designed a prediction model using the 9 genes to discriminate the two groups. Our prediction model could predict the target class without any error. From these results, we developed a prediction model using biomarkers to determine the RF radiation exposure in mouse auditory cells with perfect accuracy, which may need validation in in vivo RF-exposure models.
Keywords
support vector machine; prediction; microarray; radiofrequency radiation; auditory cell;
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