Table 4_Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.xlsx by Zuhui Pu (10931751)

“…</p>Methods<p>We analyzed scRNA-seq data from islet cells of T2D and nondiabetic (ND) patients, identifying differentially expressed genes (DEGs), especially those related to metal ion transport (RMITRGs). We employed 12 machine learning algorithms to develop predictive models and assessed immune cell infiltration using single-sample gene set enrichment analysis (ssGSEA). …”

Image 1_Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.tif by Zuhui Pu (10931751)

“…</p>Methods<p>We analyzed scRNA-seq data from islet cells of T2D and nondiabetic (ND) patients, identifying differentially expressed genes (DEGs), especially those related to metal ion transport (RMITRGs). We employed 12 machine learning algorithms to develop predictive models and assessed immune cell infiltration using single-sample gene set enrichment analysis (ssGSEA). …”

Image 2_Deciphering the role of metal ion transport-related genes in T2D pathogenesis and immune cell infiltration via scRNA-seq and machine learning.tif by Zuhui Pu (10931751)

“…</p>Methods<p>We analyzed scRNA-seq data from islet cells of T2D and nondiabetic (ND) patients, identifying differentially expressed genes (DEGs), especially those related to metal ion transport (RMITRGs). We employed 12 machine learning algorithms to develop predictive models and assessed immune cell infiltration using single-sample gene set enrichment analysis (ssGSEA). …”

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. …”

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. …”

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. …”

Supplementary file 1_Integrating bioinformatics and molecular experiments to reveal the critical role of the cellular energy metabolism-related marker PLA2G1B in COPD epithelial ce... by Jun Shi (289433)

“…</p>Material and methods<p>This research identified cell energy metabolism-related differentially expressed genes (CEM-DEGs) by collecting CEM-associated signatures from multiple public databases and integrating these markers with data from the GEO database. Subsequently, five machine learning algorithms—Boruta, Xgboost, GBM, SVM-RFE, and LASSO—were employed to screen for key variables. …”

Table 1_Integrating bioinformatics and molecular experiments to reveal the critical role of the cellular energy metabolism-related marker PLA2G1B in COPD epithelial cells.xlsx by Jun Shi (289433)

“…</p>Material and methods<p>This research identified cell energy metabolism-related differentially expressed genes (CEM-DEGs) by collecting CEM-associated signatures from multiple public databases and integrating these markers with data from the GEO database. Subsequently, five machine learning algorithms—Boruta, Xgboost, GBM, SVM-RFE, and LASSO—were employed to screen for key variables. …”

Data Sheet 1_CU Cilia – an application for image analysis by machine learning – reveals significance of cysteine cathepsin K activity for primary cilia of human thyroid epithelial... by Maren Rehders (2879318)

“…</p>Results<p>Therefore, we developed CU Cilia, i.e., a method for the detection of primary cilia and for segmentation of nuclei by machine learning-based image analysis algorithms. …”

Characterization of CD3⁺ T-cell spatial distribution in the CRC tumor microenvironment using TIPC. by Mai Chan Lau (3135303)

Data Sheet 1_Tuina therapy alleviates knee osteoarthritis by modulating PI3K/AKT/mTOR-mediated autophagy: an integrated machine learning and in vivo rat study.pdf by Zhen Wang (72451)

“…A secondary analysis was conducted using a support vector machine (SVM) algorithm to predict therapeutic effects and synergistic correlations between indicators.…”

Symbolic regression flow diagram. by Mengze Zhang (19422095)

Data-driven cluster analysis identifies three clinical phenotypes in hemodialysis patients by Canyu Chen (10703438)

“…Three distinct metabolic phenotypes emerged with exceptional stability (Adjusted Rand Index = 0.9181): high retention-inflammatory (19.5%) characterized by dialysis inadequacy and functional iron deficiency, optimal clearance (24.3%) demonstrating superior toxin removal, and intermediate-stable (56.0%) maintaining balanced parameters. …”

Desmoke-LAP: Desmoking in Laparoscopic Surgery Dataset by Yirou Pan (22560914)

Data Sheet 1_Diagnostic lncRNA biomarkers and immune-related ceRNA networks for osteonecrosis of the femoral head in metabolic syndrome identified by plasma RNA sequencing and mach... by Haoyan Sun (22172911)

The research framework. by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Optimization outcome for the elongation problem. by Shikun Chen (14625352)

“…This approach bridges the gap between model accuracy and optimization efficiency, offering a practical solution for optimizing non-differentiable machine learning models that can be extended to other tree-based ensemble algorithms. …”

Image 1_Integrated multiomics analysis identifies PHLDA1+ fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancer.jpeg by Rui Wang (52434)

“…A 7-gene mCAF-associated risk model was constructed using advanced machine learning algorithms, and the biological significance of PHLDA1 was validated through co-culture experiments and pan-cancer analyses.…”

Table 1_Integrated multiomics analysis identifies PHLDA1+ fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancer.docx by Rui Wang (52434)

“…A 7-gene mCAF-associated risk model was constructed using advanced machine learning algorithms, and the biological significance of PHLDA1 was validated through co-culture experiments and pan-cancer analyses.…”

Data Sheet 4_Integrated multiomics analysis identifies PHLDA1+ fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancer.zip by Rui Wang (52434)

“…A 7-gene mCAF-associated risk model was constructed using advanced machine learning algorithms, and the biological significance of PHLDA1 was validated through co-culture experiments and pan-cancer analyses.…”