Network toxicology and machine learning reveal key molecular targets and pathways of mono-2-ethylhexyl phthalate-induced atherosclerosis by Qiu Sun (1607974)

“…Machine learning algorithms including LASSO regression, RF, and SVM were employed to identify key targets. …”

Image 1_Construction of a glycosylation-related prognostic signature for predicting prognosis, tumor microenvironment, and immune response in soft tissue sarcoma.jpeg by Ningning Yuan (4231858)

“…Background<p>Altered glycosylation, one of the most common post-translational protein modifications, plays a critical role in the initiation and progression of soft tissue sarcoma (STS). …”

Image 2_Construction of a glycosylation-related prognostic signature for predicting prognosis, tumor microenvironment, and immune response in soft tissue sarcoma.jpeg by Ningning Yuan (4231858)

“…Background<p>Altered glycosylation, one of the most common post-translational protein modifications, plays a critical role in the initiation and progression of soft tissue sarcoma (STS). …”

DataSheet1_Application of a risk score model based on glycosylation-related genes in the prognosis and treatment of patients with low-grade glioma.docx by Binbin Zou (11358315)

“…Glycosylation, a common post-translational modification of proteins, plays a significant role in tumor transformation. …”

Image 1_Machine learning-driven exploration of therapeutic targets for atrial fibrillation-joint analysis of single-cell and bulk transcriptomes and experimental validation.tif by Yicheng Wang (810922)

“…Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) enrichment analyses were conducted to explore the functions and pathways of these DEGs. Three machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine—Recursive Feature Elimination (SVM-RFE), and random forest (RF), were applied to screen key genes related to AF. …”

Image 2_Machine learning-driven exploration of therapeutic targets for atrial fibrillation-joint analysis of single-cell and bulk transcriptomes and experimental validation.tif by Yicheng Wang (810922)

“…Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO) enrichment analyses were conducted to explore the functions and pathways of these DEGs. Three machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine—Recursive Feature Elimination (SVM-RFE), and random forest (RF), were applied to screen key genes related to AF. …”

Image 1_Integrated multi-omics elucidates PRNP knockdown-mediated chemosensitization to gemcitabine in pancreatic ductal adenocarcinoma.tif by Bing Qi (492962)

“…</p>Results<p>Our findings identify the prion protein gene (PRNP) as a key gene associated with chemoresistance. …”

Image 3_Integrated multi-omics elucidates PRNP knockdown-mediated chemosensitization to gemcitabine in pancreatic ductal adenocarcinoma.tif by Bing Qi (492962)

“…</p>Results<p>Our findings identify the prion protein gene (PRNP) as a key gene associated with chemoresistance. …”

Image 2_Integrated multi-omics elucidates PRNP knockdown-mediated chemosensitization to gemcitabine in pancreatic ductal adenocarcinoma.tif by Bing Qi (492962)

“…</p>Results<p>Our findings identify the prion protein gene (PRNP) as a key gene associated with chemoresistance. …”

Table 1_Machine learning identifies PYGM as a macrophage polarization–linked metabolic biomarker in rectal cancer prognosis.docx by Chengyuan Xu (4174936)

“…Clinical tissue analyses and functional assays confirmed that PYGM is upregulated in rectal cancer and promotes tumor cell proliferation, migration, and M2 macrophage polarization.…”

Data Sheet 1_Integrative multi-omics identifies MEIS3 as a diagnostic biomarker and immune modulator in hypertrophic cardiomyopathy.docx by Jinchen He (18929662)

“…Machine learning consistently ranked MEIS3 among the top discriminatory markers (AUC > 0.90). scRNA-seq revealed MSCs as the predominant MEIS3-expressing population in HCM myocardium. Functional enrichment implicated MEIS3 in pathways related to protein synthesis, mitochondrial metabolism, and immune modulation. …”

Table 3_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Table 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Table 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Table 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.xlsx by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Image 1_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Image 4_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tif by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

Image 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.tiff by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”

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)

“…</p>Results<p>Cross-analysis identified 186 shared DEGs between diabetes and COPD patients. Functional enrichment results demonstrate that metabolic and immune-related pathways are common features altered in both diabetes and COPD patients. …”

Data Sheet 2_Exploring common circulating diagnostic biomarkers for sleep disorders and stroke based on machine learning.csv by Hanlin Yu (17776399)

“…With WGCNA and machine-learning algorithms analyses, we found that ARL2 is an important common marker for both diseases.…”