Table 3_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Table 7_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Table 10_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Image 2_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.tif by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Table 5_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Image 3_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.tif by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Table 2_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

Table 6_Integrative single-cell and spatial transcriptomics analysis reveals FLAD1 as a regulator of the immune microenvironment in hepatocellular carcinoma.xlsx by Peng Zhu (277243)

“…A modeling approach using 92 combinations of nine machine learning algorithms was applied, producing a predictive model with good performance. …”

SHAP values for chromatin-associated proteins profiled in Hughes et al. [21] and Wang et al. [22] (A) Heatmap of absolute mean SHAP values for different algorithms DeepSHAP, Kernel... by Kashyap Chhatbar (9515350)

Predicted F1 score across temporal aggregation levels and BirdNET parameters. by Andrew J. Fairbairn (22231970)

Figure 1 from Functional Genomic Identification of Predictors of Sensitivity and Mechanisms of Resistance to Multivalent Second-Generation TRAIL-R2 Agonists by Vera Grinkevitch (15089742)

Data-Driven Design of Random Heteropolypeptides as Synthetic Polyclonal Antibodies by Haisen Zhou (13017532)

“…By combining high-throughput synthesis of selenopolypeptide derivatives with algorithm-assisted optimization, we rapidly identified SpAbs targeting human interferon-α (IFN) and tumor necrosis factor-α (TNF-α) within 2 weeks. …”

Data-Driven Design of Random Heteropolypeptides as Synthetic Polyclonal Antibodies by Haisen Zhou (13017532)

“…By combining high-throughput synthesis of selenopolypeptide derivatives with algorithm-assisted optimization, we rapidly identified SpAbs targeting human interferon-α (IFN) and tumor necrosis factor-α (TNF-α) within 2 weeks. …”

Data-Driven Design of Random Heteropolypeptides as Synthetic Polyclonal Antibodies by Haisen Zhou (13017532)

“…By combining high-throughput synthesis of selenopolypeptide derivatives with algorithm-assisted optimization, we rapidly identified SpAbs targeting human interferon-α (IFN) and tumor necrosis factor-α (TNF-α) within 2 weeks. …”

Antivirus Engines (PowerPoint) 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.…”

Table 1_Integrating GWAS and machine learning for disease risk prediction in the Taiwanese Hakka population.xlsx by Jing-Hong Xiao (22780781)

“…Incorporating feature-selected SNPs markedly improved performance: the Random Forest model achieved accuracies above 88% in cross-validation and above 85% in external validation, confirmed by 1,000× bootstrap resampling. eQTL analysis identified functional associations such as rs12121653-KDM5B and rs12121653-MGAT4EP, implicating pathways involved in metabolic and mitochondrial regulation.…”

Supplementary file 1_Integrating GWAS and machine learning for disease risk prediction in the Taiwanese Hakka population.docx by Jing-Hong Xiao (22780781)

“…Incorporating feature-selected SNPs markedly improved performance: the Random Forest model achieved accuracies above 88% in cross-validation and above 85% in external validation, confirmed by 1,000× bootstrap resampling. eQTL analysis identified functional associations such as rs12121653-KDM5B and rs12121653-MGAT4EP, implicating pathways involved in metabolic and mitochondrial regulation.…”

3D analysis of the perforated pebbles and the perforations. by Talia Yashuv (12883867)

Gene expression omnibus datasets. by Xinyi Xia (7516694)

“…Hub gene expression was verified, and survival analysis was performed using Kaplan–Meier curves. <b>Results:</b> IRI and TCMR shared 84 genes. Functional enrichment analysis revealed that inflammation played a significant role. …”

Image 1_Integrated multiomics analysis identifies PHLDA1+ fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancer.jpeg by Rui Wang (52434)

“…However, the prognostic and functional contributions of distinct CAF subtypes remain inadequately understood. …”