Image 3_Identification of three T cell-related genes as diagnostic and prognostic biomarkers for triple-negative breast cancer and exploration of potential mechanisms.tif by Zhi-Chuan He (21563657)

“…Differentially expressed genes (DEGs) between TNBC and other BRCA subtypes were intersected with T cell-related genes to identify candidate biomarkers. Machine learning algorithms were used to screen for key hub genes, which were then used to construct a logistic regression (LR) model. …”

Table 1_Identification of three T cell-related genes as diagnostic and prognostic biomarkers for triple-negative breast cancer and exploration of potential mechanisms.xlsx by Zhi-Chuan He (21563657)

“…Differentially expressed genes (DEGs) between TNBC and other BRCA subtypes were intersected with T cell-related genes to identify candidate biomarkers. Machine learning algorithms were used to screen for key hub genes, which were then used to construct a logistic regression (LR) model. …”

Image 1_Identification of three T cell-related genes as diagnostic and prognostic biomarkers for triple-negative breast cancer and exploration of potential mechanisms.tif by Zhi-Chuan He (21563657)

“…Differentially expressed genes (DEGs) between TNBC and other BRCA subtypes were intersected with T cell-related genes to identify candidate biomarkers. Machine learning algorithms were used to screen for key hub genes, which were then used to construct a logistic regression (LR) model. …”

Table 3_Identification of three T cell-related genes as diagnostic and prognostic biomarkers for triple-negative breast cancer and exploration of potential mechanisms.xlsx by Zhi-Chuan He (21563657)

“…Differentially expressed genes (DEGs) between TNBC and other BRCA subtypes were intersected with T cell-related genes to identify candidate biomarkers. Machine learning algorithms were used to screen for key hub genes, which were then used to construct a logistic regression (LR) model. …”

Table 4_Identification of three T cell-related genes as diagnostic and prognostic biomarkers for triple-negative breast cancer and exploration of potential mechanisms.xlsx by Zhi-Chuan He (21563657)

“…Differentially expressed genes (DEGs) between TNBC and other BRCA subtypes were intersected with T cell-related genes to identify candidate biomarkers. Machine learning algorithms were used to screen for key hub genes, which were then used to construct a logistic regression (LR) model. …”

Table 2_Identification of three T cell-related genes as diagnostic and prognostic biomarkers for triple-negative breast cancer and exploration of potential mechanisms.xlsx by Zhi-Chuan He (21563657)

“…Differentially expressed genes (DEGs) between TNBC and other BRCA subtypes were intersected with T cell-related genes to identify candidate biomarkers. Machine learning algorithms were used to screen for key hub genes, which were then used to construct a logistic regression (LR) model. …”

Image 2_Identification of three T cell-related genes as diagnostic and prognostic biomarkers for triple-negative breast cancer and exploration of potential mechanisms.tif by Zhi-Chuan He (21563657)

“…Differentially expressed genes (DEGs) between TNBC and other BRCA subtypes were intersected with T cell-related genes to identify candidate biomarkers. Machine learning algorithms were used to screen for key hub genes, which were then used to construct a logistic regression (LR) model. …”

Supplementary file 2_Exploring the role of ferroptosis in pemphigus: identification of diagnostic markers and regulatory mechanisms.docx by Jing Mao (148640)

“…Recent studies have suggested a potential link between ferroptosis, a form of regulated cell death dependent on iron, and various diseases. …”

Supplementary file 1_Exploring the role of ferroptosis in pemphigus: identification of diagnostic markers and regulatory mechanisms.docx by Jing Mao (148640)

“…Recent studies have suggested a potential link between ferroptosis, a form of regulated cell death dependent on iron, and various diseases. …”

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

“…Background<p>PANoptosis is a newly recognized form of programmed inflammatory cell death implicated in numerous inflammation-related diseases. …”

Supplementary file 3_Exploring the role of ferroptosis in pemphigus: identification of diagnostic markers and regulatory mechanisms.docx by Jing Mao (148640)

“…Recent studies have suggested a potential link between ferroptosis, a form of regulated cell death dependent on iron, and various diseases. …”

Data Sheet 1_Following intravenous thrombolysis, the outcome of diabetes mellitus associated with acute ischemic stroke was predicted via machine learning.docx by Xiaoqing Liu (196900)

“…The findings underscore the effectiveness of machine learning algorithms, particularly XGB, in predicting functional outcomes in diabetic AIS patients, providing clinicians with a valuable tool for treatment planning and improving patient outcome predictions based on receiver operating characteristic (ROC) analysis and accuracy assessments.…”

Image 1_Integrated bioinformatics and molecular docking analysis reveal potential hub genes and targeted therapeutics in sepsis-associated acute lung injury.jpeg by Qiongyan Chen (22406809)

“…Hub genes were screened using PPI network construction and three machine learning algorithms, and validated by Western blot. Functional enrichment, immune infiltration, and drug prediction (DSigDB) were performed, followed by molecular docking.…”

Supplementary file 1_Identification and validation of biomarkers in gastric cancer-associated membranous nephropathy: Insights from comprehensive bioinformatics analysis and machin... by Qianqian Xu (731060)

“…LASSO regression and Random Forest algorithms were used to identify hub genes associated with GC-related MN. …”

Table 1_Identification and validation of biomarkers in gastric cancer-associated membranous nephropathy: Insights from comprehensive bioinformatics analysis and machine learning.xl... by Qianqian Xu (731060)

“…LASSO regression and Random Forest algorithms were used to identify hub genes associated with GC-related MN. …”

Image 2_Spatial transcriptomic analysis of 4NQO-induced tongue cancer revealed cellular lineage diversity and evolutionary trajectory.tif by Feng Liu (72874)

“…</p>Discussion<p>This spatial transcriptomic analysis of 4NQO-induced tongue cancer provides a detailed characterization of tumor evolution and heterogeneity. …”

Image 1_Spatial transcriptomic analysis of 4NQO-induced tongue cancer revealed cellular lineage diversity and evolutionary trajectory.tif by Feng Liu (72874)

“…</p>Discussion<p>This spatial transcriptomic analysis of 4NQO-induced tongue cancer provides a detailed characterization of tumor evolution and heterogeneity. …”

Table 1_Integrated bioinformatics and molecular docking analysis reveal potential hub genes and targeted therapeutics in sepsis-associated acute lung injury.docx by Qiongyan Chen (22406809)

“…Hub genes were screened using PPI network construction and three machine learning algorithms, and validated by Western blot. Functional enrichment, immune infiltration, and drug prediction (DSigDB) were performed, followed by molecular docking.…”

Table 2_Integrated bioinformatics and molecular docking analysis reveal potential hub genes and targeted therapeutics in sepsis-associated acute lung injury.docx by Qiongyan Chen (22406809)

“…Hub genes were screened using PPI network construction and three machine learning algorithms, and validated by Western blot. Functional enrichment, immune infiltration, and drug prediction (DSigDB) were performed, followed by molecular docking.…”

Image 3_Spatial transcriptomic analysis of 4NQO-induced tongue cancer revealed cellular lineage diversity and evolutionary trajectory.tif by Feng Liu (72874)

“…</p>Discussion<p>This spatial transcriptomic analysis of 4NQO-induced tongue cancer provides a detailed characterization of tumor evolution and heterogeneity. …”