Table 2_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…This study aimed to investigate the molecular mechanisms underlying cell and metabolic reprogramming biomarkers in ARDS.</p>Methods<p>Using transcriptomic data from whole blood samples, candidate genes were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA) in conjunction with MRRGs. …”

Table 3_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…This study aimed to investigate the molecular mechanisms underlying cell and metabolic reprogramming biomarkers in ARDS.</p>Methods<p>Using transcriptomic data from whole blood samples, candidate genes were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA) in conjunction with MRRGs. …”

Table 5_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.doc by Ting Wu (106368)

“…This study aimed to investigate the molecular mechanisms underlying cell and metabolic reprogramming biomarkers in ARDS.</p>Methods<p>Using transcriptomic data from whole blood samples, candidate genes were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA) in conjunction with MRRGs. …”

Table 13_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.doc by Ting Wu (106368)

“…This study aimed to investigate the molecular mechanisms underlying cell and metabolic reprogramming biomarkers in ARDS.</p>Methods<p>Using transcriptomic data from whole blood samples, candidate genes were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA) in conjunction with MRRGs. …”

Table 6_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…This study aimed to investigate the molecular mechanisms underlying cell and metabolic reprogramming biomarkers in ARDS.</p>Methods<p>Using transcriptomic data from whole blood samples, candidate genes were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA) in conjunction with MRRGs. …”

Table 11_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…This study aimed to investigate the molecular mechanisms underlying cell and metabolic reprogramming biomarkers in ARDS.</p>Methods<p>Using transcriptomic data from whole blood samples, candidate genes were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA) in conjunction with MRRGs. …”

Application of an interpretable machine learning method to predict the risk of death during hospitalization in patients with acute myocardial infarction combined with diabetes mell... by Zhijun Bu (18544339)

“…Patients were randomly assigned to training and validation sets in an 8:2 ratio. Seven ML algorithms were used to construct models in the training set. …”

Table 1_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx by Zixiang Li (7014416)

“…</p>Methods<p>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. …”

Table 7_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…This study aimed to investigate the molecular mechanisms underlying cell and metabolic reprogramming biomarkers in ARDS.</p>Methods<p>Using transcriptomic data from whole blood samples, candidate genes were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA) in conjunction with MRRGs. …”

Image 1_Integrated analysis of N-glycosylation and Alzheimer’s disease: identifying key biomarkers and mechanisms.tif by Hao Zhang (15339)

“…</p>Methods<p>A bibliometric analysis of Web of Science literature spanning 2001–2025 was performed using VOSviewer, CiteSpace, and R. Transcriptomic data were analyzed with LIMMA to identify DEGs. …”

Supplementary file 1_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx by Zixiang Li (7014416)

“…</p>Methods<p>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. …”

Table 2_Integrative multi-omics analysis identifies a PTM-related immune signature and IRF9 as a driver in ccRCC.docx by Zixiang Li (7014416)

“…</p>Methods<p>We intersected immune-related genes, PTM-related genes, and differentially expressed genes in TCGA-KIRC to derive candidates and built a prognostic model across TCGA and E-MTAB-1980 using multiple algorithms, selecting a random survival forest-based post-translational modification-related signature (PTMRS) with the best performance. …”

Table 10_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…This study aimed to investigate the molecular mechanisms underlying cell and metabolic reprogramming biomarkers in ARDS.</p>Methods<p>Using transcriptomic data from whole blood samples, candidate genes were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA) in conjunction with MRRGs. …”

Table 4_Decoding immune-metabolic crosstalk in ARDS: a transcriptomic exploration of biomarkers, cellular dynamics, and therapeutic pathways.xlsx by Ting Wu (106368)

“…This study aimed to investigate the molecular mechanisms underlying cell and metabolic reprogramming biomarkers in ARDS.</p>Methods<p>Using transcriptomic data from whole blood samples, candidate genes were identified through differential expression analysis and weighted gene co-expression network analysis (WGCNA) in conjunction with MRRGs. …”

An explainable machine learning model in predicting vaginal birth after cesarean section by Ming Yang (109148)

“…</p> <p>Models based on ML algorithms can be used to predict VBAC. The CatBoost model showed best performance in this study. …”

Dataset on impacts of land cover and climate change on Indian birds by Vishesh L. Diengdoh (10738896)

“…Then, an ensemble of machine learning algorithms - random forest, k-nearest neighbour, artificial neural network, support vector machine, and gradient boosting model - were used to project habitat suitability of each species under present conditions (2015) and future scenarios (2100) under two Shared Socio-economic Pathways (SSP3–7.0 and SSP5–8.5). …”

Image 2_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg by Dibash Basukala (20772110)

“…</p>Methods<p>This work used retrospective anonymized breast MRI data (302 patients) from three sites employing three different software utilizing least-squares segmented algorithms and Bayesian fit to estimate 1_st order radiomics of IVIM parameters perfusion fraction (f_p), pseudo-diffusion (D_p) and tissue diffusivity (D_t). …”

Image 4_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg by Dibash Basukala (20772110)

“…</p>Methods<p>This work used retrospective anonymized breast MRI data (302 patients) from three sites employing three different software utilizing least-squares segmented algorithms and Bayesian fit to estimate 1_st order radiomics of IVIM parameters perfusion fraction (f_p), pseudo-diffusion (D_p) and tissue diffusivity (D_t). …”

Image 3_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg by Dibash Basukala (20772110)

“…</p>Methods<p>This work used retrospective anonymized breast MRI data (302 patients) from three sites employing three different software utilizing least-squares segmented algorithms and Bayesian fit to estimate 1_st order radiomics of IVIM parameters perfusion fraction (f_p), pseudo-diffusion (D_p) and tissue diffusivity (D_t). …”

Image 1_Retrospective BReast Intravoxel Incoherent Motion Multisite (BRIMM) multisoftware study.jpeg by Dibash Basukala (20772110)

“…</p>Methods<p>This work used retrospective anonymized breast MRI data (302 patients) from three sites employing three different software utilizing least-squares segmented algorithms and Bayesian fit to estimate 1_st order radiomics of IVIM parameters perfusion fraction (f_p), pseudo-diffusion (D_p) and tissue diffusivity (D_t). …”