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)

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

“…Next, we sorted the algorithms based on the average rank across all dataset/class combinations. …”

<i>Patient 1</i> GA-optimized model error. by Dmitrii Smirnov (8822324)

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<i>Patient 8</i> GA-optimized model error. by Dmitrii Smirnov (8822324)

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Algorithm verification. by Dmitrii Smirnov (8822324)

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Key gene identification based on two machine learning algorithms. by Lijun Guo (605276)

Genetic Algorithm (GA) and CAGE-based personalization block diagrams. by Dmitrii Smirnov (8822324)

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

Optimal Drug-Target Docking Visualization. by Qijun Wo (12992781)

Algorithm 2: Maintain inhibitory densities coherence. by Dimitri Rodarie (10148838)

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

Algorithm 1: Cap inhibitory densities to number of neurons. by Dimitri Rodarie (10148838)

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)

“…For classification algorithms that included multiple hyperparameter combinations (n = 47), we performed hyperparameter optimization using the respective training sets. …”

DataSheet1_Establishment and Optimization of Radiomics Algorithms for Prediction of KRAS Gene Mutation by Integration of NSCLC Gene Mutation Mutual Exclusion Information.DOCX by Jingyi Wang (550305)

“…<p>Purpose: To assess the significance of mutation mutual exclusion information in the optimization of radiomics algorithms for predicting gene mutation.…”

Comparison of presented algorithm precision to Bot et al. by Dmitrii Smirnov (8822324)

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

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Screening MMRG gene variables associated with UC based on the optimal machine learning model. by Li Li (14993)

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