DE algorithm flow. by Ling Zhao (111365)

“…In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. …”

Test results of different algorithms. by Ling Zhao (111365)

“…In the experiments, optimization metrics such as kinematic optimization rate (calculated based on the shortest path and connectivity between functional areas), space utilization rate (calculated by the ratio of room area to total usable space), and functional fitness (based on the weighted sum of users’ subjective evaluations and functional matches) all perform well. …”

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

“…<p>We predicted patient states using gene-expression and clinical predictors with feature selection (Analysis 5). …”

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)

Illustration of the BRIDES algorithm and the selection process. by Cindy Bouchard (6844979)

Subjects:

Table_1_A scheduling route planning algorithm based on the dynamic genetic algorithm with ant colony binary iterative optimization for unmanned aerial vehicle spraying in multiple... by Yangyang Liu (807797)

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)

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

UpSet diagram illustrating the overlap of DNBs selected by our method and top-ranked genes by the 13 baseline centrality-based ranking algorithms. by Fatemeh Keikha (20917848)

DataSheet1_Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference.PDF by Deniz Seçilmiş (13028616)

DataSheet1_Optimal Sparsity Selection Based on an Information Criterion for Accurate Gene Regulatory Network Inference.PDF by Deniz Seçilmiş (13028616)

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

Reproducing kernel-based semiparametric functional smoothed score estimation with binary responses by Meichen Liu (8168148)

Data_Sheet_1_Transcriptome-based selection and validation of optimal reference genes in perirenal adipose developing of goat (Capra hircus).zip by Le Zhao (483824)

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

Homomorphic binary tree. by Jianhua He (341366)

Three machine learning algorithms were further employed to select candidate key hub genes. by Zhifeng Wu (303016)

“…<p>(A, B) Based on 10-fold cross-validation, the minimum absolute shrinkage and selection operator (LASSO) regression model feature selection identified 11 genes corresponding to the lowest point of the curve, considered optimal biomarkers for studying the shared pathogenic mechanisms of AAA and periodontitis. …”