Boxplot comparing Ct values for HD in flies and clothes. by Philippe Ndzomo (17706039)

S1 Data. Bibliometric analysis. S2 Data. Estrogen level analysis. S3 Data. MicroCT quantification. S4 Data. Markers of bone metabolism. by Yan She (8639796)

Conceptual diagram of readiness for interprofessional learning. by Thales Guardia de Barros (22216025)

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Process mining, combined with decision tree analysis using machine learning, can reveal insights into various process inefficiencies, such as rework. by Mohammad Bakhtiari (11838264)

Word cloud with key elements of an Active Learning Classroom. by Mairena Sánchez-López (684092)

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Skin marks is the S1 Fig title. by Mercedes Soto-González (20422078)

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

“…SHapley Additive exPlanations interpretability analysis revealed SUV39H1 as the dominant risk contributor, while GLUL exhibited a protective effect. …”

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

“…SHapley Additive exPlanations interpretability analysis revealed SUV39H1 as the dominant risk contributor, while GLUL exhibited a protective effect. …”

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

“…SHapley Additive exPlanations interpretability analysis revealed SUV39H1 as the dominant risk contributor, while GLUL exhibited a protective effect. …”

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

“…SHapley Additive exPlanations interpretability analysis revealed SUV39H1 as the dominant risk contributor, while GLUL exhibited a protective effect. …”

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

“…SHapley Additive exPlanations interpretability analysis revealed SUV39H1 as the dominant risk contributor, while GLUL exhibited a protective effect. …”

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

“…SHapley Additive exPlanations interpretability analysis revealed SUV39H1 as the dominant risk contributor, while GLUL exhibited a protective effect. …”

Table 2_Unsupervised machine learning revealed a correlation between low-dose statins and favorable outcomes in ICH patients.docx by Chaohua Cui (1793155)

“…</p>Conclusion<p>Unsupervised machine learning revealed a correlation between low-dose statins and patient prognosis. …”

Table 1_Unsupervised machine learning revealed a correlation between low-dose statins and favorable outcomes in ICH patients.docx by Chaohua Cui (1793155)

“…</p>Conclusion<p>Unsupervised machine learning revealed a correlation between low-dose statins and patient prognosis. …”

Aims, outcomes and main results of the included studies. by Mairena Sánchez-López (684092)

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Characteristics of the studies. by Mairena Sánchez-López (684092)

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PRISMA flow chart of the study selection process. by Mairena Sánchez-López (684092)

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Learning-related changes in assembly–reader relations. by Marco N. Pompili (12288422)

Supplementary file 1_Single-cell and bulk transcriptomic analyses reveal PANoptosis-associated immune dysregulation of fibroblasts in periodontitis.zip by Erli Wu (17785482)

“…</p>Results<p>scRNA-seq analysis revealed a decreased proportion of HGFs alongside enrichment of multiple PANoptosis-related pathways in PD samples. …”

The inactivation of the acetyl-CoA acyltransferase 1 enzyme by the novel putative miRNA Oeu124369 leads to a decrease in Acetyl-CoA production and additionally, the deactivation of... by Mohammad Hossein Pakdel (19837942)