Comparison of immune cell infiltration between high-risk and low-risk sample groups. by Changquan Shang (21647625)

Calculating the TAMs-associated score. by Changquan Shang (21647625)

Data Sheet 1_Multiple analytical perspectives of mitochondrial genes in the context of preeclampsia: potential diagnostic markers.docx by Can Li (31070)

Model-based sex classification accuracies. by Kevin J. Wischnewski (21354521)

Supplementary Material 8 by Nishitha R Kumar (19750617)

“…</li><li><b>Adaboost: </b>A boosting algorithm that combines weak classifiers iteratively, refining predictions and improving the identification of antimicrobial resistance patterns.…”

Table 1_Modeling and validation in Parkinson’s disease patients with frailty.docx by Guoyang Li (6330623)

“…Eight features were identified as independent predictors of frailty: sex, age, alcohol use, Modified H&Y stage, UPDRS-IV score, HAMA score, executive function, and naming. Among all tested algorithms, logistic regression achieved the best predictive performance (AUC = 0.83 in the test set), outperforming other machine learning models.…”

Data Sheet 1_A machine-learning approach for pancreatic neoplasia classification based on plasma extracellular vesicles.pdf by Ioanna Angelioudaki (21177620)

“…Efforts to assist early diagnosis focus on liquid biopsy methods, and especially on the detection of Extracellular Vesicles (EVs) secreted from cancer cells in their microenvironment and accumulated in systemic circulation. Multiple studies explore how EVs size, surface biomarkers or content can determine their unique role and function in the recipient cell’s gene expression, metabolism and behavior affecting cancer development. …”

Table 1_The analysis of gene co-expression network and immune infiltration revealed biomarkers between triple-negative and non-triple negative breast cancer.xlsx by Yao Yi (459571)

“…</p>Methods<p>The gene co-expression network and gene ontology analyses were used to identify the differential modules and their functions based on the GEO dataset of GSE76275. The Weighted Gene Co-Expression Network Analysis (WGCNA) was used to describe the correlation patterns among genes across multiple samples. …”

Image 1_The analysis of gene co-expression network and immune infiltration revealed biomarkers between triple-negative and non-triple negative breast cancer.tif by Yao Yi (459571)

“…</p>Methods<p>The gene co-expression network and gene ontology analyses were used to identify the differential modules and their functions based on the GEO dataset of GSE76275. The Weighted Gene Co-Expression Network Analysis (WGCNA) was used to describe the correlation patterns among genes across multiple samples. …”

Image 3_The analysis of gene co-expression network and immune infiltration revealed biomarkers between triple-negative and non-triple negative breast cancer.tif by Yao Yi (459571)

“…</p>Methods<p>The gene co-expression network and gene ontology analyses were used to identify the differential modules and their functions based on the GEO dataset of GSE76275. The Weighted Gene Co-Expression Network Analysis (WGCNA) was used to describe the correlation patterns among genes across multiple samples. …”

Image 2_The analysis of gene co-expression network and immune infiltration revealed biomarkers between triple-negative and non-triple negative breast cancer.tif by Yao Yi (459571)

“…</p>Methods<p>The gene co-expression network and gene ontology analyses were used to identify the differential modules and their functions based on the GEO dataset of GSE76275. The Weighted Gene Co-Expression Network Analysis (WGCNA) was used to describe the correlation patterns among genes across multiple samples. …”

Table 2_The analysis of gene co-expression network and immune infiltration revealed biomarkers between triple-negative and non-triple negative breast cancer.xlsx by Yao Yi (459571)

“…</p>Methods<p>The gene co-expression network and gene ontology analyses were used to identify the differential modules and their functions based on the GEO dataset of GSE76275. The Weighted Gene Co-Expression Network Analysis (WGCNA) was used to describe the correlation patterns among genes across multiple samples. …”

Table 3_The analysis of gene co-expression network and immune infiltration revealed biomarkers between triple-negative and non-triple negative breast cancer.xlsx by Yao Yi (459571)

“…</p>Methods<p>The gene co-expression network and gene ontology analyses were used to identify the differential modules and their functions based on the GEO dataset of GSE76275. The Weighted Gene Co-Expression Network Analysis (WGCNA) was used to describe the correlation patterns among genes across multiple samples. …”

Image 4_The analysis of gene co-expression network and immune infiltration revealed biomarkers between triple-negative and non-triple negative breast cancer.tif by Yao Yi (459571)

“…</p>Methods<p>The gene co-expression network and gene ontology analyses were used to identify the differential modules and their functions based on the GEO dataset of GSE76275. The Weighted Gene Co-Expression Network Analysis (WGCNA) was used to describe the correlation patterns among genes across multiple samples. …”

Assessing the risk of acute kidney injury associated with a four-drug regimen for heart failure: a ten-year real-world pharmacovigilance analysis based on FAERS events by Sen Lin (182597)

“…Lastly, logistic regression was applied to investigate the relationship-value between patient characteristics and ADEs.…”