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

"…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 حسب Jingjing Chen (293564)

"…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 حسب Jingjing Chen (293564)

"…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 حسب Jingjing Chen (293564)

"…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 حسب Jingjing Chen (293564)

"…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. …"

Concept of the Loss function in the case of Supervised Machine Learning and Physics Machine Learning approach. حسب Akshansh Mishra (21001499)

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

"…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. …"

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

The AD-PSO-Guided WOA LSTM algorithm RMSE is based on the objective function compared to different algorithms. حسب Ahmed M. Elshewey (21463867)

Objective function values of different algorithms on individual problem instances. حسب Panfei Li (22379086)

Integration of three machine learning algorithms to characterize lipid metabolism-associated hub genes in sepsis and atrial fibrillation. حسب Changze Ou (22806374)

The box plot depicting the differential expression validation of the hub genes in the external dataset GSE90081. حسب Devi Soorya Narayana Sasikumar (22331743)

الموضوعات:

The Venn diagram showing the intersection of common genes between WGCNA and DEFeGs. حسب Devi Soorya Narayana Sasikumar (22331743)

الموضوعات:

The classification report of the generated LASSO, RF, and SVM models. حسب Devi Soorya Narayana Sasikumar (22331743)

الموضوعات:

a-c: (a) The nomogram model developed for the prognostic prediction of hub genes from the study. حسب Devi Soorya Narayana Sasikumar (22331743)

الموضوعات:

a-h: The expression differences of the 8 hub genes in 18 immune infiltrating cells. حسب Devi Soorya Narayana Sasikumar (22331743)

الموضوعات:

Illustrative insight into the role of NAMPT and SAT1 in ferroptosis and RA pathogenesis. حسب Devi Soorya Narayana Sasikumar (22331743)

الموضوعات:

(a) The network topology analysis to identify the optimum soft threshold power. حسب Devi Soorya Narayana Sasikumar (22331743)

الموضوعات:

The coregulatory network analysis of the hub genes from the study. حسب Devi Soorya Narayana Sasikumar (22331743)

الموضوعات:

(a) The plot is visualized to understand the association between the number of trees in our RF model and its error rate. حسب Devi Soorya Narayana Sasikumar (22331743)

الموضوعات: