Prediction of the Exposure to 1763MHz Radiofrequency Radiation Based on Gene Expression Patterns.
- Author:
Min Su LEE
1
;
Tai Qin HUANG
;
Jeong Sun SEO
;
Woong Yang PARK
Author Information
1. Biointelligence Laboratory, School of Computer Science and Engineering, Seoul National University, Seoul 110-799, Korea.
- Publication Type:In Vitro ; Original Article
- Keywords:
support vector machine;
prediction;
microarray;
radiofrequency radiation;
auditory cell
- MeSH:
Absorption;
Animals;
Cellular Phone;
Gene Expression*;
Mice;
Neurons;
Support Vector Machine;
Biomarkers
- From:Genomics & Informatics
2007;5(3):102-106
- CountryRepublic of Korea
- Language:English
-
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.
