Image 8_Interplay between tumor mutation burden and the tumor microenvironment predicts the prognosis of pan-cancer anti-PD-1/PD-L1 therapy.tif by Wuyuan Liao (21776432)

“…</p>Methods<p>We systematically collected and analyzed genomic and clinical data from patients receiving anti-PD-1/PD-L1 immunotherapy across multiple cohorts. …”

Image 2_Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.tif by Siyu Tong (12905585)

“…Using 113 machine learning algorithms and MitoCarta mitochondrial genetics data, we developed a mitochondrial gene-based diagnostic model. …”

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. …”

Table 3_Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.xlsx by Siyu Tong (12905585)

“…Using 113 machine learning algorithms and MitoCarta mitochondrial genetics data, we developed a mitochondrial gene-based diagnostic model. …”

Table 1_Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.xlsx by Siyu Tong (12905585)

“…Using 113 machine learning algorithms and MitoCarta mitochondrial genetics data, we developed a mitochondrial gene-based diagnostic model. …”

Supplementary file 1_Gene expression profile in colon cancer therapeutic resistance and its relationship with the tumor microenvironment.docx by Priscila Galvão Doria (22518647)

“…The following algorithms were used: i. Limma was applied to identify differentially expressed genes; ii. …”

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_Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.tif by Siyu Tong (12905585)

“…Using 113 machine learning algorithms and MitoCarta mitochondrial genetics data, we developed a mitochondrial gene-based diagnostic model. …”

Image 3_Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.jpeg by Siyu Tong (12905585)

“…Using 113 machine learning algorithms and MitoCarta mitochondrial genetics data, we developed a mitochondrial gene-based diagnostic model. …”

Table 2_Development and validation of a machine learning-driven mitochondrial gene signature for the diagnosis of breast cancer.xlsx by Siyu Tong (12905585)

“…Using 113 machine learning algorithms and MitoCarta mitochondrial genetics data, we developed a mitochondrial gene-based diagnostic model. …”

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. …”

Image 1_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. …”

Table 3_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validat... by Guiling Wu (6031019)

“…Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. …”

Table 6_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validat... by Guiling Wu (6031019)

“…Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. …”

Table 9_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validat... by Guiling Wu (6031019)

“…Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. …”

Table 10_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental valida... by Guiling Wu (6031019)

“…Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. …”

Table 1_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validat... by Guiling Wu (6031019)

“…Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. …”

Table 14_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental valida... by Guiling Wu (6031019)

“…Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. …”

Table 11_Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental valida... by Guiling Wu (6031019)

“…Candidate biomarkers were screened using machine learning algorithms combined with 12 cytoHubba algorithms, and a diagnostic model for T2DM-related MAFLD was constructed and evaluated.The CIBERSORT method was used to investigate immune cell infiltration in MAFLD and the immunological significance of central genes. …”