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. …”

Search for acetylcholinesterase inhibitors by computerized screening of approved drug compounds by T.A. Materova (22770138)

Turkish_native_goat_genotypes by Yalçın YAMAN (20209833)

“…An ensemble feature-importance analysis, evaluated through 10,000 permutations, identified 31 FDR-significant SNPs representing markers consistently associated with MAP status. Functional annotation indicated involvement of immune-related processes such as cytokine–receptor signalling, antigen presentation, glycan-mediated T-cell regulation, and NF-κB–linked inflammatory pathways. …”

Image 3_SUMOylation-related genes define prognostic subtypes in stomach adenocarcinoma: integrating single-cell analysis and machine learning analyses.tif by Kaiping Luo (14494751)

“…Immune infiltration, pathway enrichment identified key SRGs, and in vitro functional assays were validated.</p>Results<p>Two molecular subtypes (A/B) with distinct SUMOylation patterns, survival outcomes (log-rank p < 0.001), and immune microenvironments were identified. …”

Image 1_SUMOylation-related genes define prognostic subtypes in stomach adenocarcinoma: integrating single-cell analysis and machine learning analyses.tif by Kaiping Luo (14494751)

“…Immune infiltration, pathway enrichment identified key SRGs, and in vitro functional assays were validated.</p>Results<p>Two molecular subtypes (A/B) with distinct SUMOylation patterns, survival outcomes (log-rank p < 0.001), and immune microenvironments were identified. …”

Image 2_SUMOylation-related genes define prognostic subtypes in stomach adenocarcinoma: integrating single-cell analysis and machine learning analyses.tif by Kaiping Luo (14494751)

“…Immune infiltration, pathway enrichment identified key SRGs, and in vitro functional assays were validated.</p>Results<p>Two molecular subtypes (A/B) with distinct SUMOylation patterns, survival outcomes (log-rank p < 0.001), and immune microenvironments were identified. …”

Image 4_SUMOylation-related genes define prognostic subtypes in stomach adenocarcinoma: integrating single-cell analysis and machine learning analyses.tif by Kaiping Luo (14494751)

“…Immune infiltration, pathway enrichment identified key SRGs, and in vitro functional assays were validated.</p>Results<p>Two molecular subtypes (A/B) with distinct SUMOylation patterns, survival outcomes (log-rank p < 0.001), and immune microenvironments were identified. …”

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. Functional enrichment, immune infiltration, and drug prediction (DSigDB) were performed, followed by molecular docking.…”

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. Functional enrichment, immune infiltration, and drug prediction (DSigDB) were performed, followed by molecular docking.…”

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. Functional enrichment, immune infiltration, and drug prediction (DSigDB) were performed, followed by molecular docking.…”

Table 1_Development and validation of a machine-learning-based model for identification of genes associated with sepsis-associated acute kidney injury.docx by Chen Lin (95910)

“…Finally, we used functional enrichment analysis to identify potential therapeutic agents for AKI.…”

Data Sheet 3_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). Functional enrichment and immunoinfiltration analyses were performed to explore biological pathways and immune cell interactions. …”

Data Sheet 2_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). Functional enrichment and immunoinfiltration analyses were performed to explore biological pathways and immune cell interactions. …”

Table1_“Dictionary of immune responses” reveals the critical role of monocytes and the core target IRF7 in intervertebral disc degeneration.xls by Peichuan Xu (19862055)

“…IREA revealed that monocytes in IDD polarize into an IFN-a1 and IFN-b enriched Mono-a state, potentially intensifying inflammation. …”

Table 1_Key gene screening and diagnostic model establishment for acute type a aortic dissection.xlsx by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). Functional enrichment and immunoinfiltration analyses were performed to explore biological pathways and immune cell interactions. …”

Image2_“Dictionary of immune responses” reveals the critical role of monocytes and the core target IRF7 in intervertebral disc degeneration.jpeg by Peichuan Xu (19862055)

“…IREA revealed that monocytes in IDD polarize into an IFN-a1 and IFN-b enriched Mono-a state, potentially intensifying inflammation. …”

Data Sheet 4_Key gene screening and diagnostic model establishment for acute type a aortic dissection.csv by Yue Pan (267516)

“…</p>Methods<p>Transcriptome datasets from the Gene Expression Omnibus (GEO) database were analyzed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), and machine learning algorithms (SVM, Random Forest, LASSO regression). Functional enrichment and immunoinfiltration analyses were performed to explore biological pathways and immune cell interactions. …”