Relative performance of classification algorithms using gene-expression and clinical predictors and performing feature selection. by Stephen R. Piccolo (8367780)

“…We used nested cross validation to estimate which features would be optimal for each algorithm in each training set. …”

Gene-set overlap results for top 50 genes according to average rank across feature-selection algorithms for GSE46691. by Stephen R. Piccolo (8367780)

Gene-set overlap results for top 50 genes according to average rank across feature-selection algorithms for GSE10320. by Stephen R. Piccolo (8367780)

Top 50 genes according to average rank across feature-selection algorithms for GSE10320 and GSE46691. by Stephen R. Piccolo (8367780)

IFS curves of two classification algorithms based on feature list yielded by MCFS. by Zhan Dong Li (12481390)

Summary of predictive performance per dataset when using gene-expression and clinical predictors and performing feature selection. by Stephen R. Piccolo (8367780)

Summary of predictive performance per dataset when using gene-expression and clinical predictors and performing feature selection with hyperparameter optimization. by Stephen R. Piccolo (8367780)

Expression values and standard errors of genes within each patient group under the optimal feature filtering method (ALOGA < 128) used for (blue bars) distinguishing each individua... by Song Cui (553627)

IFS performance with different classification algorithms and number of top features in the list yielded by MCFS. by Zhan Dong Li (12481390)

Relative predictive performance when training on gene-expression predictors alone vs. using clinical predictors alone or gene-expression predictors in combination with clinical pre... by Stephen R. Piccolo (8367780)

Summary of predictive performance per dataset when using gene-expression and clinical predictors and performing hyperparameter optimization. by Stephen R. Piccolo (8367780)

Summary of predictive performance per dataset when using gene-expression predictors. by Stephen R. Piccolo (8367780)

Summary of predictive performance per dataset when using gene-expression and clinical predictors. by Stephen R. Piccolo (8367780)

Comparison of ranks for classification algorithms across performance metrics. by Stephen R. Piccolo (8367780)

Tradeoff between execution time and predictive performance for classification algorithms. by Stephen R. Piccolo (8367780)

Initialization of variables in the APDCA algorithm. by Yangsong He (13909371)

Subjects:

Table2_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

Table1_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

Table3_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)

Table4_Identification of Pan-Cancer Biomarkers Based on the Gene Expression Profiles of Cancer Cell Lines.XLSX by ShiJian Ding (11764646)