Data Sheet 1_The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.zip by Binyu Wang (7375019)

“…Objective<p>Lung adenocarcinoma (LUAD) continues to be a primary cause of cancer-related mortality globally, highlighting the urgent need for novel insights finto its molecular mechanisms. …”

Data Sheet 2_The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.pdf by Binyu Wang (7375019)

“…Objective<p>Lung adenocarcinoma (LUAD) continues to be a primary cause of cancer-related mortality globally, highlighting the urgent need for novel insights finto its molecular mechanisms. …”

Data Sheet 3_The role and machine learning analysis of mitochondrial autophagy-related gene expression in lung adenocarcinoma.pdf by Binyu Wang (7375019)

“…Objective<p>Lung adenocarcinoma (LUAD) continues to be a primary cause of cancer-related mortality globally, highlighting the urgent need for novel insights finto its molecular mechanisms. …”

Table 3_Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.xlsx by Linyuan Wang (359295)

“…Differential gene analysis was performed using the “limma” package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms were also applied to identify key biomarkers. …”

Table 4_Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.docx by Linyuan Wang (359295)

“…Differential gene analysis was performed using the “limma” package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms were also applied to identify key biomarkers. …”

Table 5_Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.docx by Linyuan Wang (359295)

“…Differential gene analysis was performed using the “limma” package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms were also applied to identify key biomarkers. …”

Table 1_Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.xlsx by Linyuan Wang (359295)

“…Differential gene analysis was performed using the “limma” package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms were also applied to identify key biomarkers. …”

Data Sheet 1_Comprehensive bioinformatics analysis identifies metabolic and immune-related diagnostic biomarkers shared between diabetes and COPD using multi-omics and machine lear... by Qianqian Liang (1521904)

“…Multiple machine learning algorithms identified 4 shared biomarkers for COPD and diabetes, including CADPS, EDNRB, THBS4 and TMEM27. …”

Table 2_Combining WGCNA and machine learning to identify mechanisms and biomarkers of hyperthyroidism and atrial fibrillation.xlsx by Linyuan Wang (359295)

“…Differential gene analysis was performed using the “limma” package, and overlapping genes shared by both diseases were identified through weighted gene co-expression network analysis (WGCNA), followed by functional enrichment analysis. Machine learning algorithms were also applied to identify key biomarkers. …”

AP-2α 相关研究 by Ya-Hong Wang (21080642)

“…</a>The algorithms for Figures B, C, and D are the same as Figure A.…”

DataSheet1_Identification of potential auxin response candidate genes for soybean rapid canopy coverage through comparative evolution and expression analysis.zip by Deisiany Ferreira Neres (10484791)

“…Further development of this and similar algorithms for defining and quantifying tissue- and phenotype-specificity in gene expression may allow expansion of diversity in valuable phenotypes in important crops.…”

Data Sheet 1_Identification and validation of cellular senescence-related genes and immune cell infiltration characteristics in intervertebral disc degeneration.pdf by Hao Li (31608)

“…SRDEGs were analyzed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). A protein–protein interaction (PPI) network was also drawn, and the hub SRDEGs were obtained using 11 different algorithms. …”

Supplementary file 1_Socioeconomic status and lifestyle as factors of multimorbidity among older adults in China: results from the China Health and Retirement Longitudinal Survey.d... by Wei Gong (112494)

“…Socioeconomic and functional variables were dominant factors associated with multimorbidity, suggesting structural roots of health inequality. …”

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

“…</p>Objective<p>This study aims to construct a machine learning-based prognostic signature using exercise-related immune genes (EIGs) to predict prognosis in HCC.…”

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

“…</p>Objective<p>This study aims to construct a machine learning-based prognostic signature using exercise-related immune genes (EIGs) to predict prognosis in HCC.…”

Data Sheet 1_Case Report: Myelodysplastic/myeloproliferative neoplasm with concurrent SF3B1, ASXL1, JAK2 and CBL mutations and <15% bone marrow ringed sideroblasts.docx by Yifan Wang (380120)

“…Comprehensive genomic profiling revealed a unique quadruple mutation signature: ASXL1 p.G646Wfs*12 (9.8% VAF), JAK2 p.R683G (17.5%), and CBL p.R149Q (16.2%), with preserved karyotype. …”

Table 1_Case Report: Myelodysplastic/myeloproliferative neoplasm with concurrent SF3B1, ASXL1, JAK2 and CBL mutations and <15% bone marrow ringed sideroblasts.xlsx by Yifan Wang (380120)

“…Comprehensive genomic profiling revealed a unique quadruple mutation signature: ASXL1 p.G646Wfs*12 (9.8% VAF), JAK2 p.R683G (17.5%), and CBL p.R149Q (16.2%), with preserved karyotype. …”

Data Sheet 2_Case Report: Myelodysplastic/myeloproliferative neoplasm with concurrent SF3B1, ASXL1, JAK2 and CBL mutations and <15% bone marrow ringed sideroblasts.pdf by Yifan Wang (380120)

“…Comprehensive genomic profiling revealed a unique quadruple mutation signature: ASXL1 p.G646Wfs*12 (9.8% VAF), JAK2 p.R683G (17.5%), and CBL p.R149Q (16.2%), with preserved karyotype. …”

Image 4_Identification of lactylation-related biomarkers in osteoporosis from transcriptome and single-cell data.jpeg by Jiafeng Peng (22116253)

“…Subsequently, nomograms, functional enrichment analyses, immune infiltration analyses, regulatory network construction, drug prediction, and molecular docking were performed to characterize the functional and clinical significance of the biomarkers. …”

Table 4_Identification of lactylation-related biomarkers in osteoporosis from transcriptome and single-cell data.xlsx by Jiafeng Peng (22116253)

“…Subsequently, nomograms, functional enrichment analyses, immune infiltration analyses, regulatory network construction, drug prediction, and molecular docking were performed to characterize the functional and clinical significance of the biomarkers. …”