Image 3_Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.pdf by Yi Li (1144)

“…Additionally, clinical samples were used to validate the expression patterns of these hub genes, and scRNA-seq data were utilized to analyze the cell types expressing these genes and to explore potential mechanisms of cell communication.…”

Table 2_Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.docx by Yi Li (1144)

“…Additionally, clinical samples were used to validate the expression patterns of these hub genes, and scRNA-seq data were utilized to analyze the cell types expressing these genes and to explore potential mechanisms of cell communication.…”

Image 1_Validation of the Somnolyzer 24×7 automatic scoring system in children with suspected obstructive sleep apnea.tif by Ignacio Boira (21562568)

“…Introduction<p>Manual scoring of polysomnography data is a laborious and complex process. Automatic scoring by current computer algorithms shows high agreement with manual scoring. …”

Supplementary file 1_Plasma FGF2 and YAP1 as novel biomarkers for MCI in the elderly: analysis via bioinformatics and clinical study.docx by Yejing Zhao (22123588)

“…To address this gap, datasets GSE29378 and GSE12685 were selected to screen differentially expressed genes (DEGs), and hub genes were identified by different algorithms. A total of 350 DEGs were identified in bioinformatics data mining. …”

Table 1_Validation of the Somnolyzer 24×7 automatic scoring system in children with suspected obstructive sleep apnea.docx by Ignacio Boira (21562568)

“…Introduction<p>Manual scoring of polysomnography data is a laborious and complex process. Automatic scoring by current computer algorithms shows high agreement with manual scoring. …”

Image 1_Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.pdf by Yi Li (1144)

“…Additionally, clinical samples were used to validate the expression patterns of these hub genes, and scRNA-seq data were utilized to analyze the cell types expressing these genes and to explore potential mechanisms of cell communication.…”

Table 2_Cellular hierarchy framework based on single-cell and bulk RNA sequencing reveals fatty acid metabolic biomarker MYDGF as a therapeutic target for ccRCC.xlsx by Ning Wang (108353)

“…Functional enrichment algorithms, including AUCell, UCell, singscore, ssGSEA, and AddModuleScore, along with hdWGCNA analysis, were used to identify hub genes influencing high FAM of ccRCC. …”

Table 1_Cellular hierarchy framework based on single-cell and bulk RNA sequencing reveals fatty acid metabolic biomarker MYDGF as a therapeutic target for ccRCC.docx by Ning Wang (108353)

“…Functional enrichment algorithms, including AUCell, UCell, singscore, ssGSEA, and AddModuleScore, along with hdWGCNA analysis, were used to identify hub genes influencing high FAM of ccRCC. …”

The information of samples. by Junhao Wang (751227)

“…Three machine learning algorithms were applied for identification of characteristic gene for IC. …”

Hub genes in the cyan module. by Junhao Wang (751227)

“…Three machine learning algorithms were applied for identification of characteristic gene for IC. …”

Hub genes in the pink module. by Junhao Wang (751227)

“…Three machine learning algorithms were applied for identification of characteristic gene for IC. …”

Hub genes in the blue module. by Junhao Wang (751227)

“…Three machine learning algorithms were applied for identification of characteristic gene for IC. …”

Presentation 1_Deep learning radiomics nomogram predicts lymph node metastasis in laryngeal squamous cell carcinoma.pptx by Yun Liang (383275)

“…Radiomics features were extracted from CT images, and 7 machine learning algorithms were used to develop 7 radiomics models, which were combined with deep learning features extracted from the ResNet50 deep learning network to form deep learning radiomics (DLR) models. …”

Image 4_Revealing key regulatory factors in lung adenocarcinoma: the role of epigenetic regulation of autophagy-related genes from transcriptomics, scRNA-seq, and machine learning.... by Xianchang Zeng (9329117)

“…</p>Conclusion<p>In this study, we utilized bulk and single-cell transcriptomic data to uncover the potential molecular mechanisms of A-ERGs in lung cancer. …”

Image 3_Revealing key regulatory factors in lung adenocarcinoma: the role of epigenetic regulation of autophagy-related genes from transcriptomics, scRNA-seq, and machine learning.... by Xianchang Zeng (9329117)

“…</p>Conclusion<p>In this study, we utilized bulk and single-cell transcriptomic data to uncover the potential molecular mechanisms of A-ERGs in lung cancer. …”

Presentation 3_Deep learning radiomics nomogram predicts lymph node metastasis in laryngeal squamous cell carcinoma.pptx by Yun Liang (383275)

“…Radiomics features were extracted from CT images, and 7 machine learning algorithms were used to develop 7 radiomics models, which were combined with deep learning features extracted from the ResNet50 deep learning network to form deep learning radiomics (DLR) models. …”

Image 5_Revealing key regulatory factors in lung adenocarcinoma: the role of epigenetic regulation of autophagy-related genes from transcriptomics, scRNA-seq, and machine learning.... by Xianchang Zeng (9329117)

“…</p>Conclusion<p>In this study, we utilized bulk and single-cell transcriptomic data to uncover the potential molecular mechanisms of A-ERGs in lung cancer. …”

Image 1_Decoding monocyte heterogeneity in sepsis: a single-cell apoptotic signature for immune stratification and guiding precision therapy.tif by Wenjuan Duan (11875262)

“…</p>Methods<p>We integrated single-cell and bulk transcriptomic data from four independent cohorts. A machine learning pipeline incorporating SVM, RF, XGB, and GLM algorithms was used to identify hub genes associated with monocyte apoptosis. …”

Table 1_A novel molecular classification system based on the molecular feature score identifies patients sensitive to immune therapy and target therapy.xlsx by Yang Li (7082)

“…Subsequently, machine learning algorithms were used to predict the classifications and prognoses. …”

Image 2_Revealing key regulatory factors in lung adenocarcinoma: the role of epigenetic regulation of autophagy-related genes from transcriptomics, scRNA-seq, and machine learning.... by Xianchang Zeng (9329117)

“…</p>Conclusion<p>In this study, we utilized bulk and single-cell transcriptomic data to uncover the potential molecular mechanisms of A-ERGs in lung cancer. …”