Genomics & Informatics

  • 제4권2호
  • /
  • Pages.71-76
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  • 2006
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  • 1598-866X(pISSN)
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  • 2234-0742(eISSN)
한국유전체학회 (Korea Genome Organization)
권호 표지

Prediction of Exposure to 1763MHz Radiofrequency Radiation Using Support Vector Machine Algorithm in Jurkat Cell Model System

  • Huang Tai-Qin (Ilchun Molecular Medicine Institute and Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine) ;
  • Lee Min-Su (Department of Computer Science and Engineering, Ewha Womans University) ;
  • Bae Young-Joo (Macrogen Inc.) ;
  • Park Hyun-Seok (Department of Computer Science and Engineering, Ewha Womans University) ;
  • Park Woong-Yang (Ilchun Molecular Medicine Institute and Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine) ;
  • Seo Jeong-Sun (Ilchun Molecular Medicine Institute and Department of Biochemistry and Molecular Biology, Seoul National University College of Medicine)
  • 발행 : 2006.06.01
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초록

We have investigated biological responses to radiofrequency (RF) radiation in in vitro and in vivo models. By measuring the levels of heat shock proteins as well as the activation of mitogen activated protein kinases (MAPKs), we could not detect any differences upon RF exposure. In this study, we used more sensitive method to find the molecular responses to RF radiation. Jurkat, human T-Iymphocyte cells were exposed to 1763 MHz RF radiation at an average specific absorption rate (SAR) of 10 W/kg for one hour and harvested immediately (R0) or after five hours (R5). From the profiles of 30,000 genes, we selected 68 differentially expressed genes among sham (S), R0 and R5 groups using a random-variance F-test. Especially 45 annotated genes were related to metabolism, apoptosis or transcription regulation. Based on support vector machine (SVM) algorithm, we designed prediction model using 68 genes to discriminate three groups. Our prediction model could predict the target class of 19 among 20 examples exactly (95% accuracy). From these data, we could select the 68 biomarkers to predict the RF radiation exposure with high accuracy, which might need to be validated in in vivo models.

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참고문헌

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