Table 1_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.docx by Xudong Shen (205653)

“…Among 101 machine learning models, S100A4, ARPC1B, and CEBPD were identified as the most significant interacting genes linking diabetes and kidney stones. …”

Image 5_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

“…Among 101 machine learning models, S100A4, ARPC1B, and CEBPD were identified as the most significant interacting genes linking diabetes and kidney stones. …”

Data Sheet 1_Exploring the molecular mechanisms of phthalates in the comorbidity of preeclampsia and depression by integrating multiple datasets.zip by Xinpeng Tian (646275)

“…Machine learning algorithms were applied to select core diagnostic genes, followed by validation in independent cohorts. …”

Image 3_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif by Xingchao Liu (3501161)

“…Although neutrophils are recognized as key regulators of LIHC progression, their functional heterogeneity and metabolic drivers are not yet fully understood.…”

Image 5_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif by Xingchao Liu (3501161)

“…Although neutrophils are recognized as key regulators of LIHC progression, their functional heterogeneity and metabolic drivers are not yet fully understood.…”

Image 4_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif by Xingchao Liu (3501161)

“…Although neutrophils are recognized as key regulators of LIHC progression, their functional heterogeneity and metabolic drivers are not yet fully understood.…”

Image 2_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif by Xingchao Liu (3501161)

“…Although neutrophils are recognized as key regulators of LIHC progression, their functional heterogeneity and metabolic drivers are not yet fully understood.…”

Image 1_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif by Xingchao Liu (3501161)

“…Although neutrophils are recognized as key regulators of LIHC progression, their functional heterogeneity and metabolic drivers are not yet fully understood.…”

Image 6_Dysregulated arginine metabolism is associated with pro-tumor neutrophil polarization in liver cancer.tif by Xingchao Liu (3501161)

“…Although neutrophils are recognized as key regulators of LIHC progression, their functional heterogeneity and metabolic drivers are not yet fully understood.…”

Data Sheet 1_Air pollution-related immune gene prognostic signature for hepatocellular carcinoma: network toxicology, machine learning and multi-omics analysis.pdf by Lei Pu (608561)

“…APIGPS constructed by 7 APIGs (CDC25C, MELK, ATG4B, SLC2A1, CDC25B, APEX1, GLS), demonstrated robust predictive ability independent of clinical features. …”

Table 1_Air pollution-related immune gene prognostic signature for hepatocellular carcinoma: network toxicology, machine learning and multi-omics analysis.xlsx by Lei Pu (608561)

“…APIGPS constructed by 7 APIGs (CDC25C, MELK, ATG4B, SLC2A1, CDC25B, APEX1, GLS), demonstrated robust predictive ability independent of clinical features. …”

Antivirus Engines by Paul A. Gagniuc (1818325)

“…Materialul combină fundamente teoretice cu exemple aplicate, prezentând modele, algoritmi și structuri de date utilizate în detecția amenințărilor informatice, oferind o imagine completă asupra modului în care soluțiile antivirus sunt concepute și implementate în practică.</p><p dir="ltr"><b>References</b></p><p dir="ltr">Paul A. Gagniuc.…”

Image 5_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…Drug sensitivity analyses revealed distinct therapeutic vulnerabilities between subgroups. Functional assays confirmed that MAP1B promotes proliferation, migration, and invasion in GBM cells, reinforcing its oncogenic role.…”

Image 3_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…Drug sensitivity analyses revealed distinct therapeutic vulnerabilities between subgroups. Functional assays confirmed that MAP1B promotes proliferation, migration, and invasion in GBM cells, reinforcing its oncogenic role.…”

Table 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx by Liren Fang (22489516)

“…Drug sensitivity analyses revealed distinct therapeutic vulnerabilities between subgroups. Functional assays confirmed that MAP1B promotes proliferation, migration, and invasion in GBM cells, reinforcing its oncogenic role.…”

Image 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…Drug sensitivity analyses revealed distinct therapeutic vulnerabilities between subgroups. Functional assays confirmed that MAP1B promotes proliferation, migration, and invasion in GBM cells, reinforcing its oncogenic role.…”

Image 4_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…Drug sensitivity analyses revealed distinct therapeutic vulnerabilities between subgroups. Functional assays confirmed that MAP1B promotes proliferation, migration, and invasion in GBM cells, reinforcing its oncogenic role.…”

Table 1_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.xlsx by Liren Fang (22489516)

“…Drug sensitivity analyses revealed distinct therapeutic vulnerabilities between subgroups. Functional assays confirmed that MAP1B promotes proliferation, migration, and invasion in GBM cells, reinforcing its oncogenic role.…”

Image 2_Single-cell and machine learning-based pyroptosis-related gene signature predicts prognosis and immunotherapy response in glioblastoma.tif by Liren Fang (22489516)

“…Drug sensitivity analyses revealed distinct therapeutic vulnerabilities between subgroups. Functional assays confirmed that MAP1B promotes proliferation, migration, and invasion in GBM cells, reinforcing its oncogenic role.…”

Construction of a novel radioresistance-related signature for prediction of prognosis, immune microenvironment and anti-tumour drug sensitivity in non-small cell lung cancer by Yanliang Chen (15235486)

“…</p> <p>Ninety-nine RRRGs were screened by intersecting the results of DEGs and WGCNA, then 11 hub RRRGs associated with survival were identified using machine learning algorithms (LASSO and RSF). Subsequently, an eight-gene (<i>APOBEC3B, DOCK4, IER5L, LBH, LY6K, RERG, RMDN2</i> and <i>TSPAN2</i>) risk score model was established and demonstrated to be an independent prognostic factor in NSCLC on the basis of Cox regression analysis. …”