Data Sheet 2_Novel diagnostic biomarkers associated with macrophage-microglia in spinal cord injury.pdf by Manyi Zheng (22221913)

“…From 200 genes associated with critical WGCNA modules, three hub genes, including EMP3, GNGT2, and SGPL1, were identified through four ML algorithms as differentially expressed before and after injury. …”

Supplementary file 1_Novel diagnostic biomarkers associated with macrophage-microglia in spinal cord injury.xlsx by Manyi Zheng (22221913)

“…From 200 genes associated with critical WGCNA modules, three hub genes, including EMP3, GNGT2, and SGPL1, were identified through four ML algorithms as differentially expressed before and after injury. …”

Table 2_Novel diagnostic biomarkers associated with macrophage-microglia in spinal cord injury.xlsx by Manyi Zheng (22221913)

“…From 200 genes associated with critical WGCNA modules, three hub genes, including EMP3, GNGT2, and SGPL1, were identified through four ML algorithms as differentially expressed before and after injury. …”

Supplementary file 2_Novel diagnostic biomarkers associated with macrophage-microglia in spinal cord injury.xlsx by Manyi Zheng (22221913)

“…From 200 genes associated with critical WGCNA modules, three hub genes, including EMP3, GNGT2, and SGPL1, were identified through four ML algorithms as differentially expressed before and after injury. …”

Table 3_Novel diagnostic biomarkers associated with macrophage-microglia in spinal cord injury.xlsx by Manyi Zheng (22221913)

“…From 200 genes associated with critical WGCNA modules, three hub genes, including EMP3, GNGT2, and SGPL1, were identified through four ML algorithms as differentially expressed before and after injury. …”

Image 1_Identification of signature genes and subtypes for heart failure diagnosis based on machine learning.tif by Yanlong Zhang (3159840)

“…</p>Methods<p>HF datasets were acquired from the Gene Expression Omnibus (GEO) database (GSE57338), and through the application of bioinformatics and machine-learning algorithms. …”

Data Sheet 1_Correlation between oral microbial characteristics and overall bone density of Postmenopausal women based on macrogenomic analysis.pdf by Man Liu (697951)

“…KEGG pathway analysis was used to reveal variations in microbial functions. Based on these analyses, predictive models for bone density status were constructed using LASSO regression and random forest algorithms.…”

Supplementary materials for PhD thesis - "Ontology mapping with intelligent agents on the Semantic Web : the theory and practice of agent belief and consenus building" by Miklos Nagy (22007560)

“…I propose to manage uncertainty, a focal issue that is inherent to data interpretation, through Dempster-Shafer belief functions. Furthermore, the research explores how conflicts in belief functions can be treated with voting mechanisms that establish trust in agent beliefs, while rejecting beliefs that cannot be trusted in a certain situation. …”

Table 2_Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer.xlsx by Qiang Luo (387063)

“…Prognostic modeling of PC was developed using 14 machine learning algorithms, with performance validated through long-term survival metrics, functional enrichment, immune infiltration analysis, and drug sensitivity profiling. …”

Supplementary file 1_Identification of glycolysis-related clusters and immune cell infiltration in hepatic fibrosis progression using machine learning models and experimental valid... by Guanglin Xiao (18113302)

“…Integrated weighted gene co-expression network analysis (WGCNA) with six machine learning algorithms to identify core GRGs genes associated with HF progression, and systematically characterized their biological functions and immunoregulatory roles through immune infiltration assessment, functional enrichment, consensus clustering, and single-cell differential state analysis. …”

Construction of a novel prognostic model based on lncRNAs-related to DNA damage repair for predicting the prognosis of clear cell renal cell carcinoma by Peng Chen (6514)

“…</p> <p>RNA-seq data and clinical data of ccRCC were downloaded from public databases. Subsequently, Pearson correlation analysis and differential expression analysis were performed to identify DElncRNAs-related to DDR. …”

Table 3_Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer.xlsx by Qiang Luo (387063)

“…Prognostic modeling of PC was developed using 14 machine learning algorithms, with performance validated through long-term survival metrics, functional enrichment, immune infiltration analysis, and drug sensitivity profiling. …”

Table 4_Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer.xlsx by Qiang Luo (387063)

“…Prognostic modeling of PC was developed using 14 machine learning algorithms, with performance validated through long-term survival metrics, functional enrichment, immune infiltration analysis, and drug sensitivity profiling. …”

Table 5_Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer.xlsx by Qiang Luo (387063)

“…Prognostic modeling of PC was developed using 14 machine learning algorithms, with performance validated through long-term survival metrics, functional enrichment, immune infiltration analysis, and drug sensitivity profiling. …”

Data Sheet 1_Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer.pdf by Qiang Luo (387063)

“…Prognostic modeling of PC was developed using 14 machine learning algorithms, with performance validated through long-term survival metrics, functional enrichment, immune infiltration analysis, and drug sensitivity profiling. …”

Table 1_Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer.xlsx by Qiang Luo (387063)

“…Prognostic modeling of PC was developed using 14 machine learning algorithms, with performance validated through long-term survival metrics, functional enrichment, immune infiltration analysis, and drug sensitivity profiling. …”

Table 6_Innate immune cell barrier-related genes inform precision prognosis in pancreatic cancer.xlsx by Qiang Luo (387063)

“…Prognostic modeling of PC was developed using 14 machine learning algorithms, with performance validated through long-term survival metrics, functional enrichment, immune infiltration analysis, and drug sensitivity profiling. …”

Table 1_Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury.docx by Chen Lin (95910)

“…</p>Methods<p>We used two sepsis (GSE57065 and GSE28750) and three AKI (GSE30718, GSE139061, and GSE67401) datasets from the NCBI Gene Expression Omnibus (GEO) for model development and validation, and performed batch effect mitigation, differential gene, and functional enrichment analysis using R software packages. …”

Table 3_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.xlsx by Shenglong Wang (569676)

“…Time-series clustering revealed sustained upregulation of IQGAP1 from day 7 onward in the subacute phase. Functional enrichment analyses (GO, KEGG, GSVA, and GSEA) implicated IQGAP1 in cytoskeleton remodeling, immune regulation, and metabolic reprogramming. …”

Table 5_Identification of genes associated with disulfidptosis in the subacute phase of spinal cord injury and analysis of potential therapeutic targets.xlsx by Shenglong Wang (569676)

“…Time-series clustering revealed sustained upregulation of IQGAP1 from day 7 onward in the subacute phase. Functional enrichment analyses (GO, KEGG, GSVA, and GSEA) implicated IQGAP1 in cytoskeleton remodeling, immune regulation, and metabolic reprogramming. …”