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- W2364152001 abstract "Statistics method of two-side t-test combined with a new feature selection method,genetic algorithm-partial least squares algorithm,are used in this paper for the feature extraction for SELDI-TOF MS ovarian cancer data.4 m/z values are obtained from the original 15154 m/z values and the support vector machines(SVM) classifier works well based on these 4 m/z values.Both 3-fold cross validation and leave-one-out cross validation are used for checking the pattern's stability.The result of leave-one-out cross validation is 95.26%.The results indicated that genetic algorithm-partial least squares algorithm is an efficient feature extraction method for proteomics data and potential ovarian cancer biomarkers may exist in the 4 m/z values selected in this paper." @default.
- W2364152001 created "2016-06-24" @default.
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- W2364152001 date "2007-01-01" @default.
- W2364152001 modified "2023-09-30" @default.
- W2364152001 title "Genetic algorithm-partial least squares algorithm as the feature selection method for proteomics data of ovarian cancer" @default.
- W2364152001 hasPublicationYear "2007" @default.
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