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⏲ Duration: 4 min 23 sec
✓ Published: 20-Jun-2019
✓ Published: 20-Jun-2019
Description:
To screen differentially expressed genes quickly and efficiently in breast cancer, two gene microarray datasets of breast cancer, GSE15852 and GSE45255, were downloaded from GEO. By combining the Logistic Regression and Random Forest algorithm, this paper proposed a novel method named LR-RF to select differentially expressed genes of breast cancer on microarray data by the Bonferroni test of FWER error measure. Comparing with Logistic Regression and Random Forest, our study shows that LR FR has
To screen differentially expressed genes quickly and efficiently in breast cancer, two gene microarray datasets of breast cancer, GSE15852 and GSE45255, were downloaded from GEO. By combining the Logistic Regression and Random Forest algorithm, this paper proposed a novel method named LR-RF to select differentially expressed genes of breast cancer on microarray data by the Bonferroni test of FWER error measure. Comparing with Logistic Regression and Random Forest, our study shows that LR FR has
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