Image 1_Machine learning identifies neutrophil extracellular traps-related biomarkers for acute ischemic stroke diagnosis.tif by Haipeng Zhang (3413288)

“…Four hub genes—SRC, TLR8, FCAR, and HIF1A—were identified using LASSO and RF algorithms. A diagnostic model based on these genes yielded area under the curve (AUC) values of 0.880 in the training dataset and 0.936 in the validation dataset. …”

Table 1_Machine learning identifies neutrophil extracellular traps-related biomarkers for acute ischemic stroke diagnosis.docx by Haipeng Zhang (3413288)

“…Four hub genes—SRC, TLR8, FCAR, and HIF1A—were identified using LASSO and RF algorithms. A diagnostic model based on these genes yielded area under the curve (AUC) values of 0.880 in the training dataset and 0.936 in the validation dataset. …”

Table 1_Construction of a glycosylation-related prognostic signature for predicting prognosis, tumor microenvironment, and immune response in soft tissue sarcoma.docx by Ningning Yuan (4231858)

“…A comprehensive machine learning framework integrating 101 algorithms was applied to construct a glycosyltransferase-based prognostic signature. …”

Image 3_Construction of a glycosylation-related prognostic signature for predicting prognosis, tumor microenvironment, and immune response in soft tissue sarcoma.jpeg by Ningning Yuan (4231858)

“…A comprehensive machine learning framework integrating 101 algorithms was applied to construct a glycosyltransferase-based prognostic signature. …”

Image 1_Construction of a glycosylation-related prognostic signature for predicting prognosis, tumor microenvironment, and immune response in soft tissue sarcoma.jpeg by Ningning Yuan (4231858)

“…A comprehensive machine learning framework integrating 101 algorithms was applied to construct a glycosyltransferase-based prognostic signature. …”

Image 2_Construction of a glycosylation-related prognostic signature for predicting prognosis, tumor microenvironment, and immune response in soft tissue sarcoma.jpeg by Ningning Yuan (4231858)

“…A comprehensive machine learning framework integrating 101 algorithms was applied to construct a glycosyltransferase-based prognostic signature. …”

Table 1_Exploring fecal microbiota signatures associated with immune response and antibiotic impact in NSCLC: insights from metagenomic and machine learning approaches.docx by Wenjie Han (1812673)

“…Antibiotic exposure significantly influenced the abundance and functional potential of these key taxa. KEGG-based functional analysis revealed the enrichment of amino acid metabolism pathways in responders. …”

Image 1_Machine learning-driven exploration of therapeutic targets for atrial fibrillation-joint analysis of single-cell and bulk transcriptomes and experimental validation.tif by Yicheng Wang (810922)

“…Three machine learning algorithms identified six key genes for AF. The nomogram model based on these six genes demonstrated excellent diagnostic performance with an AUC of 0.97. …”

Image 2_Machine learning-driven exploration of therapeutic targets for atrial fibrillation-joint analysis of single-cell and bulk transcriptomes and experimental validation.tif by Yicheng Wang (810922)

“…Three machine learning algorithms identified six key genes for AF. The nomogram model based on these six genes demonstrated excellent diagnostic performance with an AUC of 0.97. …”

Table 2_Integration of single-cell sequencing and machine learning identifies key macrophage-associated genetic signatures in lumbar disc degeneration.xlsx by Hongxing Zhang (209372)

“…A panel of 101 machine learning algorithms was employed to screen diagnostic genes, with ROC curves used for validation. …”

Table 1_Integration of single-cell sequencing and machine learning identifies key macrophage-associated genetic signatures in lumbar disc degeneration.xlsx by Hongxing Zhang (209372)

“…A panel of 101 machine learning algorithms was employed to screen diagnostic genes, with ROC curves used for validation. …”

Image 1_Exercise-related immune gene signature for hepatocellular carcinoma: machine learning and multi-omics analysis.pdf by Cheng Pu (14791088)

“…Furthermore, we conducted molecular subtyping, qRT-PCR, biological functions, immune infiltration, drug sensitivity, and single cell analyses on EIGPS.…”

Table 4_Integration of single-cell sequencing and machine learning identifies key macrophage-associated genetic signatures in lumbar disc degeneration.xlsx by Hongxing Zhang (209372)

“…A panel of 101 machine learning algorithms was employed to screen diagnostic genes, with ROC curves used for validation. …”

Data Sheet 1_Exercise-related immune gene signature for hepatocellular carcinoma: machine learning and multi-omics analysis.xlsx by Cheng Pu (14791088)

“…Furthermore, we conducted molecular subtyping, qRT-PCR, biological functions, immune infiltration, drug sensitivity, and single cell analyses on EIGPS.…”

Table 3_Integration of single-cell sequencing and machine learning identifies key macrophage-associated genetic signatures in lumbar disc degeneration.xlsx by Hongxing Zhang (209372)

“…A panel of 101 machine learning algorithms was employed to screen diagnostic genes, with ROC curves used for validation. …”

Data Sheet 2_Integration of single-cell sequencing and machine learning identifies key macrophage-associated genetic signatures in lumbar disc degeneration.pdf by Hongxing Zhang (209372)

“…A panel of 101 machine learning algorithms was employed to screen diagnostic genes, with ROC curves used for validation. …”

Data Sheet 1_Integration of single-cell sequencing and machine learning identifies key macrophage-associated genetic signatures in lumbar disc degeneration.pdf by Hongxing Zhang (209372)

“…A panel of 101 machine learning algorithms was employed to screen diagnostic genes, with ROC curves used for validation. …”

Table 1_Identification of crosstalk genes and diagnostic biomarkers in systemic sclerosis associated sarcopenia through integrative analysis and machine learning.docx by Yanfang Wu (206076)

“…Crosstalk genes (CGs) were identified using least absolute shrinkage and selection operator (LASSO) regularization, ensemble decision trees, and support vector machine-based feature selection. Machine learning algorithms were employed to construct a predictive scoring model and to assess the diagnostic value of key biomarkers. …”

Data Sheet 1_Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods.docx by Ankita Lawarde (16544943)

“…Our comparative analysis demonstrated that models based on miRNA outperformed those using mRNA or lncRNA classifiers.…”

Image 1_Integrated multi-omics analysis and machine learning identify G protein-coupled receptor-related signatures for diagnosis and clinical benefits in soft tissue sarcoma.jpeg by Duo Wang (1787634)

“…Moreover, the GPR-TME classifier as the prognosis model was constructed and further performed for immune infiltration, functional enrichment, somatic mutation, immunotherapy response prediction, and scRNA-seq analyses.…”