Table 3_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>Results<p>Our analysis identified 1953 DEGs between T2D and ND patients, with the Stepglm[backward] plus GBM model demonstrating high predictive accuracy and identifying 13 hub RMITRGs. Twelve protein structures were predicted using AlphaFold 3, revealing potential functional conformations. …”

Table 2_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>Results<p>Our analysis identified 1953 DEGs between T2D and ND patients, with the Stepglm[backward] plus GBM model demonstrating high predictive accuracy and identifying 13 hub RMITRGs. Twelve protein structures were predicted using AlphaFold 3, revealing potential functional conformations. …”

Table 1_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>Results<p>Our analysis identified 1953 DEGs between T2D and ND patients, with the Stepglm[backward] plus GBM model demonstrating high predictive accuracy and identifying 13 hub RMITRGs. Twelve protein structures were predicted using AlphaFold 3, revealing potential functional conformations. …”

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>Results<p>Our analysis identified 1953 DEGs between T2D and ND patients, with the Stepglm[backward] plus GBM model demonstrating high predictive accuracy and identifying 13 hub RMITRGs. Twelve protein structures were predicted using AlphaFold 3, revealing potential functional conformations. …”

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>Results<p>Our analysis identified 1953 DEGs between T2D and ND patients, with the Stepglm[backward] plus GBM model demonstrating high predictive accuracy and identifying 13 hub RMITRGs. Twelve protein structures were predicted using AlphaFold 3, revealing potential functional conformations. …”

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>Results<p>Our analysis identified 1953 DEGs between T2D and ND patients, with the Stepglm[backward] plus GBM model demonstrating high predictive accuracy and identifying 13 hub RMITRGs. Twelve protein structures were predicted using AlphaFold 3, revealing potential functional conformations. …”

Table 1_Extracellular microRNAs modulate human microglial function through TLR8.docx by Hannah Weidling (14422749)

“…</p>Methods<p>Using a machine learning algorithm and the disease-linked database PhenoMiR, we identified Alzheimer’s disease (AD)- and glioma-associated miRNAs as ligands for TLR7 and TLR8. …”

A synopsis of the research design and flowchart. by Ravinder Sharma (13154079)

“…Subsequently, using the MCODE algorithm, we identified 6 hub genes—ATN1, JPH3, TBP, VPS13A, DMD, and HTT—as core candidates. …”

Significance of variables in the RF model. by Chen Chen (6544)

“…This study investigated the association between serum protein expression profiles and cognitive variability in a healthy Thai population using machine learning algorithms. …”

Performance metrics of the RF model. by Chen Chen (6544)

“…This study investigated the association between serum protein expression profiles and cognitive variability in a healthy Thai population using machine learning algorithms. …”

Demographic data of the study population. by Chen Chen (6544)

“…This study investigated the association between serum protein expression profiles and cognitive variability in a healthy Thai population using machine learning algorithms. …”

Overall working procedure of this study. by Chen Chen (6544)

“…This study investigated the association between serum protein expression profiles and cognitive variability in a healthy Thai population using machine learning algorithms. …”

Gene Ontology terms of the 6 hub genes. by Ravinder Sharma (13154079)

Screening of high-efficiency chloroplast-targeting peptides. by Chonprakun Thagun (12120578)

Key Genes Screening and LASSO Risk Model Construction. by Jianguo Ma (2119684)

SHAP values for H3K4me3, co-occupying PRC1 (RING1B) and PRC2 (SUZ12) components and their catalytic marks across multiple time points following RING1B degradation. by Kashyap Chhatbar (9515350)

SHAP values for co-occupying PRC1 (RING1B) and PRC2 (SUZ12) components and their catalytic marks. by Kashyap Chhatbar (9515350)

SHAP values for SET1A, H3K4me3 and ZC3H4. by Kashyap Chhatbar (9515350)

Figure 3 from Membrane-bound Heat Shock Protein mHsp70 Is Required for Migration and Invasion of Brain Tumors by Maxim Shevtsov (15023874)

“…<b>C–E,</b> Analysis of the mass spectrometry data from isolated lipid rafts. Protein functional groups identified using the STRING database in the proteome of lipid rafts from three tumor zones (Supplementary Fig. …”

Highly thermostable carboxylic acid reductases generated by ancestral sequence reconstruction (dataset) by A Thomas (841916)

“…Here, we employed ancestral sequence reconstruction (ASR) – a burgeoning engineering tool that can identify stabilizing but enzymatically neutral mutations throughout a protein. We used a three-algorithm approach to reconstruct functional ancestors of the Mycobacterial and Nocardial CAR1 orthologues. …”