Methodological flowchart outlining the study’s workflow. by Ezekiel Ahn (6697256)

“…<p>The process begins with data acquisition from field trials. …”

Cringe - Emotional manipulation as a tool of political control in Albanian digital campaigns (2025) by Edlira Gugu (21793610)

“…All personal data processed in compliance with EU GDPR regulations.…”

Image 1_Immune-molecular nexus in reproductive disorders: mechanisms linking POI and RSA.pdf by Chen Chen (6544)

“…</p>Objective<p>This study aims to identify new central genes and potential therapeutic drugs for RSA and POI by integrating multi transcriptome data and machine learning algorithms.</p>Methods<p>This study utilized RNA sequencing data from patients with POI and RSA to identify key hub genes associated with these diseases. …”

Table 1_Next-generation sequencing of mitochondrial DNA reveals pathogenic variants and protective haplogroup D4 in esophageal cancer.xlsx by Xiucheng Jiang (18849564)

“…Within the protein-coding regions, 14 variants were classified as deleterious based on predictions from 13 independent bioinformatic algorithms. …”

Agentic Architecture: Synthesising Complexity for Regenerative Futures by Alisa Andrasek (18028282)

“…It introduces a novel workflow linking n-dimensional voxelised data, multimodal generative AI, and generative algorithmic systems, including simulations of electromagnetic fields, site-adaptive Multi-Agent Systems, and field-based logics. …”

Data Sheet 1_Identification and validation of the nicotine metabolism-related signature of bladder cancer by bioinformatics and machine learning.zip by Yating Zhan (5048351)

“…Integrative machine learning combination based on 10 machine learning algorithms was used for the construction of robust signature. …”

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

“…A SUMOylation Risk Score (SRS) model was developed using 69 machine learning models across 10 algorithms, with performance evaluated by C-index and AUC. …”

Image 3_Prognostic significance of calcium-related genes in lung adenocarcinoma and the role of TNNC1 in macrophage polarization and erlotinib resistance.jpeg by Jian Feng (317890)

“…</p>Conclusion<p>This study developed a reliable prognostic signature based on nine CRPGs for predicting LUAD patient outcomes. …”

Image 2_Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.pdf by Yi Li (1144)

“…Subsequently, the LASSO and SVM algorithms were employed to identify shared hub genes between the two diseases. …”

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

“…A SUMOylation Risk Score (SRS) model was developed using 69 machine learning models across 10 algorithms, with performance evaluated by C-index and AUC. …”

Table 1_A multi-cohort validated OXPHOS signature predicts survival and immune profiles in grade II/III glioma patients.xlsx by Jun Mou (4113313)

“…The immune cell composition and tumor microenvironment (TME) characteristics were assessed using ESTIMATE, MCPcounter, and CIBERSORT algorithms. Based on prognostic DEGs, we constructed a four-gene prognostic signature (MAOB, IGFBP2, SERPINA1, and LGR6).…”

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

“…Functional enrichment analysis, GSVA, and ssGSEA were performed. Machine learning algorithms including LASSO regression, RF, and SVM were employed to identify key targets. …”

Table 1_Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.docx by Yi Li (1144)

“…Subsequently, the LASSO and SVM algorithms were employed to identify shared hub genes between the two diseases. …”

Image 1_Triglyceride–glucose index as a key predictor of ARDS in acute pancreatitis: SHAP analysis reveals its critical role in risk stratification.pdf by Ju Luo (3233037)

“…The SHAP method further confirmed that the TyG index serves as a significant predictor for the development of ARDS in patients with acute pancreatitis.…”

Image 1_Immune-related RELT drives clear cell renal cell carcinoma progression through JAK/STAT signaling pathway activation.tiff by Yini Wang (410389)

“…</p>Methods<p>The relationship between differential expression of RELT in ccRCC and clinical prognosis was investigated based on data from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) databases. …”

Image 2_Immune-related RELT drives clear cell renal cell carcinoma progression through JAK/STAT signaling pathway activation.tiff by Yini Wang (410389)

“…</p>Methods<p>The relationship between differential expression of RELT in ccRCC and clinical prognosis was investigated based on data from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) databases. …”

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

“…A SUMOylation Risk Score (SRS) model was developed using 69 machine learning models across 10 algorithms, with performance evaluated by C-index and AUC. …”

Table 3_Integrating machine learning and single-cell sequencing to identify shared biomarkers in type 1 diabetes mellitus and clear cell renal cell carcinoma.xlsx by Yi Li (1144)

“…Subsequently, the LASSO and SVM algorithms were employed to identify shared hub genes between the two diseases. …”

Table 1_Prognostic significance of calcium-related genes in lung adenocarcinoma and the role of TNNC1 in macrophage polarization and erlotinib resistance.xlsx by Jian Feng (317890)

“…</p>Conclusion<p>This study developed a reliable prognostic signature based on nine CRPGs for predicting LUAD patient outcomes. …”

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

“…A SUMOylation Risk Score (SRS) model was developed using 69 machine learning models across 10 algorithms, with performance evaluated by C-index and AUC. …”