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자료유형
학술대회자료
저자정보
원지혜 (성균관대학교) 박현진 (기초과학연구원)
저널정보
대한전자공학회 대한전자공학회 학술대회 2020년도 대한전자공학회 하계종합학술대회 논문집
발행연도
2020.8
수록면
1,174 - 1,177 (4page)

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The age at onset (AAO) is an important determinant of clinical phenotypes in Parkinson’s disease (PD). The AAO of PD is influenced by genetic factors that could be explored well using an emergent analysis approach of neuroimaging genetics. The approach jointly analyzes imaging and genetic data. This study aimed to identify single nucleotide polymorphisms (SNPs) associated with the AAO in PD by applying the imaging genetics and to construct an analytical model for predicting the AAO in PD. We obtained 146 neuroimaging and SNP data from an open research database. Fractional anisotropy of diffusion MRI was used as the intermediate phenotype in the imaging genetics approach to identify the associated SNPs with the AAO of PD. The identified SNPs were used to construct an analytical model to predict the AAO of PD. Another classifier model was built using the SNPs to classify the AAO of PD into four classes. The analytical and classifier models were trained and tested in a five-fold cross-validation. The identified SNPs explained the AAO of PD well (adjusted R2 of 0. 689 over five training folds). Our proposed model predicted the AAO of PD and classified the four subgroups divided by the onset age. In detail, our regression model showed meaningful correlation (r = 0.754, p < {10}^{-4}; averaged over five folds) between the predicted and real AAO of PD. Our classification model showed a mean accuracy of 56.8% over five left out test folds. Our models showed potential for predicting the AAO of PD. Our finding could have a significant impact in preventive medicine in PD because our analytical model is entirely made from SNPs which can be measured accurately after birth possibly before onset.

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Abstract
Ⅰ. 서론
Ⅱ. 본론
Ⅲ. 구현
Ⅲ. 결론 및 향후 연구 방향
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UCI(KEPA) : I410-ECN-0101-2020-569-001130732