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학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
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연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2022.4
- 수록면
- 1,439 - 1,448 (10page)
- DOI
- https://doi.org/10.1016/j.net.2021.10.020
이용수
초록· 키워드
오류제보하기
Background: We investigated the feasibility of in vitro radiosensitivity prediction with gene expressionusing deep learning.
Methods: A microarray gene expression of the National Cancer Institute-60 (NCI-60) panel was acquiredfrom the Gene Expression Omnibus. The clonogenic surviving fractions at an absorbed dose of 2 Gy (SF2)from previous publications were used to measure in vitro radiosensitivity. The radiosensitivity predictionmodel was based on the convolutional neural network. The 6-fold cross-validation (CV) was applied totrain and validate the model. Then, the leave-one-out cross-validation (LOOCV) was applied by using thelarge-errored samples as a validation set, to determine whether the error was from the high bias of thefolded CV. The criteria for correct prediction were defined as an absolute error<0.01 or a relativeerror<10%.
Results: Of the 174 triplicated samples of NCI-60, 171 samples were correctly predicted with the foldedCV. Through an additional LOOCV, one more sample was correctly predicted, representing a predictionaccuracy of 98.85% (172 out of 174 samples). The average relative error and absolute errors of 172correctly predicted samples were 1.351±1.875% and 0.00596±0.00638, respectively.
Conclusion: We demonstrated the feasibility of a deep learning-based in vitro radiosensitivity predictionusing gene expression
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