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Feature selection and prediction modeling of drug responsiveness in Pharmacogenomics
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약물유전체학에서 약물반응 예측모형과 변수선택 방법

논문 기본 정보

Type
Academic journal
Author
Kyuhwan Kim (중앙대학교) Wonkuk Kim (중앙대학교)
Journal
한국통계학회 응용통계연구 Vol.34 No.2 KCI Accredited Journals
Published
2021.4
Pages
153 - 166 (14page)

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Feature selection and prediction modeling of drug responsiveness in Pharmacogenomics
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A main goal of pharmacogenomics studies is to predict individual’s drug responsiveness based on high dimensional genetic variables. Due to a large number of variables, feature selection is required in order to reduce the number of variables. The selected features are used to construct a predictive model using machine learning algorithms. In the present study, we applied several hybrid feature selection methods such as combinations of logistic regression, ReliefF, TurF, random forest, and LASSO to a next generation sequencing data set of 400 epilepsy patients. We then applied the selected features to machine learning methods including random forest, gradient boosting, and support vector machine as well as a stacking ensemble method. Our results showed that the stacking model with a hybrid feature selection of random forest and ReliefF performs better than with other combinations of approaches. Based on a 5-fold cross validation partition, the mean test accuracy value of the best model was 0.727 and the mean test AUC value of the best model was 0.761. It also appeared that the stacking models outperform than single machine learning predictive models when using the same selected features.
#AUC #ensemble method #machine learning #random forest #stacking #앙상블 #머신러닝 #랜덤포레스트 #스태킹

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Abstract
1. 서론
2. 방법론
3. 데이터 분석
4. 분석결과
5. 결론
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