Steps of the Logistics Capacity Game Algorithm. by Dandan Wang (286632)

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Performance comparison between meta-gradient driven and benchmark algorithms. by Dandan Wang (286632)

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Crowding distance and IGD-driven grey wolf reinforcement learning approach for multi-objective agile earth observation satellite scheduling by He Wang (250350)

Performance comparison of ICP algorithms. by Mengyuan Yan (14200322)

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Research on Vehicle Detection Algorithms Based on Deep Learning in Complex Environments by Xiaodan Li (22309438)

(a) Prediction using traditional algorithm. (b) Prediction using optimization algorithm. by Dandan Wang (286632)

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Machine learning-driven identification of shared hub genes. by Weiming Cai (135737)

Point cloud data registration using the original ICP algorithm. by Mengyuan Yan (14200322)

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Efficiency of imputation algorithms. by Yongqi Bu (20477564)

Registered and fused point cloud data using improved ICP algorithm. by Mengyuan Yan (14200322)

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Equilibrium convergence performance of strategy decomposition algorithm. by Dandan Wang (286632)

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Supplementary Information for Machine Learning-Driven Optimization of Hardness and Toughness in High-Entropy Alloy Coatings Based on Composition and Descriptor by Kehong Zhong (21210977)

An illustration of the Deepwalk algorithm operation involves a random walk on the graph, resulting in a “walk” sequence, which is then utilized to construct a graph. by Xiaobing Deng (1622233)

Table 1_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Table 4_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Table 5_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Table 2_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

Table 3_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.xlsx by Jingjing Chen (293564)

“…Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”

(a) Traditional economic benefits. (b) Economic benefits after algorithm optimization. by Dandan Wang (286632)

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Data Sheet 1_Explainable machine learning reveals ribosome biogenesis biomarkers in preeclampsia risk prediction.docx by Jingjing Chen (293564)

“…Functional enrichment analyses delineated the dysregulation of pathways, while weighted gene co-expression network analysis identified hub genes within ribosome biogenesis-associated modules. A multi-algorithm machine learning framework was employed to optimize predictive performance, with model interpretability achieved through SHapley Additive exPlanations and diagnostic accuracy validated by receiver operating characteristic curves. …”