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)

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)

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.…”

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. …”

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

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

Bayesian Clustering via Fusing of Localized Densities by Alexander Dombowsky (20289372)

Schematic representation of the relationship between expected profits of the retailer and supplier. by Minchao Zheng (20558832)

Order quantity and profit of the supply chain under different unit disposal costs. by Minchao Zheng (20558832)

The order quantity and profit of the supply chain under different unit production costs. by Minchao Zheng (20558832)

The optimal order quantity and the wholesale price under different <i>ϕ</i>. by Minchao Zheng (20558832)

Data Sheet 1_Case Report: Myelodysplastic/myeloproliferative neoplasm with concurrent SF3B1, ASXL1, JAK2 and CBL mutations and <15% bone marrow ringed sideroblasts.docx by Yifan Wang (380120)

“…Comprehensive genomic profiling revealed a unique quadruple mutation signature: ASXL1 p.G646Wfs*12 (9.8% VAF), JAK2 p.R683G (17.5%), and CBL p.R149Q (16.2%), with preserved karyotype. Functional analyses demonstrated mutation-specific pathobiological crosstalk: 1) SF3B1-mediated mitochondrial iron mislocalization (ALAS2 splicing defects, ABCB7 downregulation) synergized with ASXL1-driven epigenetic repression of erythroid transcription factors (GATA1, KLF1), exacerbating anemia; 2) JAK2 p.R683G’s partial kinase activation combined with CBL-dependent RAS/MAPK signaling sustained thrombocytosis through megakaryocytic hyperplasia. …”

Table 1_Case Report: Myelodysplastic/myeloproliferative neoplasm with concurrent SF3B1, ASXL1, JAK2 and CBL mutations and <15% bone marrow ringed sideroblasts.xlsx by Yifan Wang (380120)

“…Comprehensive genomic profiling revealed a unique quadruple mutation signature: ASXL1 p.G646Wfs*12 (9.8% VAF), JAK2 p.R683G (17.5%), and CBL p.R149Q (16.2%), with preserved karyotype. Functional analyses demonstrated mutation-specific pathobiological crosstalk: 1) SF3B1-mediated mitochondrial iron mislocalization (ALAS2 splicing defects, ABCB7 downregulation) synergized with ASXL1-driven epigenetic repression of erythroid transcription factors (GATA1, KLF1), exacerbating anemia; 2) JAK2 p.R683G’s partial kinase activation combined with CBL-dependent RAS/MAPK signaling sustained thrombocytosis through megakaryocytic hyperplasia. …”

Data Sheet 2_Case Report: Myelodysplastic/myeloproliferative neoplasm with concurrent SF3B1, ASXL1, JAK2 and CBL mutations and <15% bone marrow ringed sideroblasts.pdf by Yifan Wang (380120)

“…Comprehensive genomic profiling revealed a unique quadruple mutation signature: ASXL1 p.G646Wfs*12 (9.8% VAF), JAK2 p.R683G (17.5%), and CBL p.R149Q (16.2%), with preserved karyotype. Functional analyses demonstrated mutation-specific pathobiological crosstalk: 1) SF3B1-mediated mitochondrial iron mislocalization (ALAS2 splicing defects, ABCB7 downregulation) synergized with ASXL1-driven epigenetic repression of erythroid transcription factors (GATA1, KLF1), exacerbating anemia; 2) JAK2 p.R683G’s partial kinase activation combined with CBL-dependent RAS/MAPK signaling sustained thrombocytosis through megakaryocytic hyperplasia. …”

MCCN Case Study 2 - Spatial projection via modelled data by Donald Hobern (21435904)

“…This repository contains Jupyter notebooks to demonstrate the functionality of the MCCN data cube components.</p><p dir="ltr">The dataset contains input files for the case study (source_data), RO-Crate metadata (ro-crate-metadata.json), results from the case study (results), and Jupyter Notebook (MCCN-CASE 2.ipynb)</p><h4><b>Research Activity Identifier (RAiD)</b></h4><p dir="ltr">RAiD: https://doi.org/10.26292/8679d473</p><h4><b>Case Studies</b></h4><p dir="ltr">This repository contains code and sample data for the following case studies. …”

Gated Feedforward Network (GDFN) Structure. by Xuanming Wang (22184274)

“…By labeling image edges and noise, and utilizing neighborhood based wavelet coefficient shrinkage algorithm, the noise interference in the image is effectively reduced; preliminary enhancement was performed on the denoised image, using Retinex algorithm combined with bilateral filtering method to estimate illuminance, and Sigmoid function was used to enhance the reflection area, improving the overall visual effect of the image. …”

Pixel perception module structure. by Xuanming Wang (22184274)

“…By labeling image edges and noise, and utilizing neighborhood based wavelet coefficient shrinkage algorithm, the noise interference in the image is effectively reduced; preliminary enhancement was performed on the denoised image, using Retinex algorithm combined with bilateral filtering method to estimate illuminance, and Sigmoid function was used to enhance the reflection area, improving the overall visual effect of the image. …”

Infrared night vision image test sample. by Xuanming Wang (22184274)

“…By labeling image edges and noise, and utilizing neighborhood based wavelet coefficient shrinkage algorithm, the noise interference in the image is effectively reduced; preliminary enhancement was performed on the denoised image, using Retinex algorithm combined with bilateral filtering method to estimate illuminance, and Sigmoid function was used to enhance the reflection area, improving the overall visual effect of the image. …”