Image_2_Prognostic value of COL10A1 and its correlation with tumor-infiltrating immune cells in urothelial bladder cancer: A comprehensive study based on bioinformatics and clinica... by Xiaoming Wang (187053)

“…GO and KEGG enrichment along with GSEA analyses were performed to reveal the biological functions and potential regulatory mechanisms of COL10A1 based on the gene co-expression network. …”

Table 1_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics ana... by Zhaokai Zhou (15239078)

“…The study identified four pCAFs-based BLCA distinct subtypes with different molecular, functional, and immunologic characteristics. C3 exhibited an immune-rich subtype accompanied by poor clinical prognosis, cell death pathway enrichment, higher expression of MHC molecules and co-stimulatory/co-inhibitory molecules. …”

Table_2_Prognostic value of COL10A1 and its correlation with tumor-infiltrating immune cells in urothelial bladder cancer: A comprehensive study based on bioinformatics and clinica... by Xiaoming Wang (187053)

“…GO and KEGG enrichment along with GSEA analyses were performed to reveal the biological functions and potential regulatory mechanisms of COL10A1 based on the gene co-expression network. …”

Table_3_Prognostic value of COL10A1 and its correlation with tumor-infiltrating immune cells in urothelial bladder cancer: A comprehensive study based on bioinformatics and clinica... by Xiaoming Wang (187053)

“…GO and KEGG enrichment along with GSEA analyses were performed to reveal the biological functions and potential regulatory mechanisms of COL10A1 based on the gene co-expression network. …”

Image_3_Prognostic value of COL10A1 and its correlation with tumor-infiltrating immune cells in urothelial bladder cancer: A comprehensive study based on bioinformatics and clinica... by Xiaoming Wang (187053)

“…GO and KEGG enrichment along with GSEA analyses were performed to reveal the biological functions and potential regulatory mechanisms of COL10A1 based on the gene co-expression network. …”

Image 2_Characterization of cancer-related fibroblasts in bladder cancer and construction of CAFs-based bladder cancer classification: insights from single-cell and multi-omics ana... by Zhaokai Zhou (15239078)

“…The study identified four pCAFs-based BLCA distinct subtypes with different molecular, functional, and immunologic characteristics. C3 exhibited an immune-rich subtype accompanied by poor clinical prognosis, cell death pathway enrichment, higher expression of MHC molecules and co-stimulatory/co-inhibitory molecules. …”

Table_1_Prognostic value of COL10A1 and its correlation with tumor-infiltrating immune cells in urothelial bladder cancer: A comprehensive study based on bioinformatics and clinica... by Xiaoming Wang (187053)

“…GO and KEGG enrichment along with GSEA analyses were performed to reveal the biological functions and potential regulatory mechanisms of COL10A1 based on the gene co-expression network. …”

Image_1_Prognostic value of COL10A1 and its correlation with tumor-infiltrating immune cells in urothelial bladder cancer: A comprehensive study based on bioinformatics and clinica... by Xiaoming Wang (187053)

“…GO and KEGG enrichment along with GSEA analyses were performed to reveal the biological functions and potential regulatory mechanisms of COL10A1 based on the gene co-expression network. …”

Polyanion sodium cathode materials dataset by Martin Hoffmann Petersen (13626778)

“…The Perdew-Burke-Ernzerhof (PBE) functional with Hubbard-U corrections were applied was utilized for all calculations. …”

Table 1_Explainable machine learning prediction of internet addiction among Chinese primary and middle school children and adolescents: a longitudinal study based on positive youth... by Jiahe Liu (9096353)

“…Our study aimed to examine the risk factors associated with IA among Chinese children and adolescents and leverage explainable machine learning (ML) algorithms to predict IA status at the time of assessment, based on Young’s Internet Addiction Test.…”

Sub-millimetre quantitative T1 mapping using inversion-recovery EPI and application for cortical depth-dependent fMRI at 7 Tesla by Sriranga Kashyap (9546227)

“…The cortical depths are usually determined on an anatomical image which makes good co-registration with the distorted functional data crucial in such studies. …”

Centrality measures definition. by Ugo Lomoio (16624608)

“…<div><p>The structure and sequence of proteins strongly influence their biological functions. New models and algorithms can help researchers in understanding how the evolution of sequences and structures is related to changes in functions. …”

Data used to drive the Double Layer Carbon Model in the Qinling Mountains. by Huiwen Li (17705280)

“…., 2022a), to estimate the spatiotemporal dynamics of SOC in different soil layers and further evaluate the impacts of different climate response functions on SOC estimates in the Qinling Mountains. …”

<b>Figures for Protein-Protein interaction (PPI) network of differentially acetylated proteins</b><b> </b><b>by Aspirin during differentiation of THP-1 cell towards macrophage</b> by Zi-Hui Ma (14118552)

“…The protein-protein interaction (PPI) networks were generated using STRING (H. sapiens; confidence score > 0.7) and visualized in Cytoscape 3.2.1. to elucidate how Aspirin-driven acetylated proteins functionally coordinate within cellular systems. The PPI network was further analyzed to identify densely interconnected functional clusters/modules using topological clustering algorithms. …”

<b>Protein-Protein interaction (PPI) network of differentially acetylated proteins</b><b> by Aspirin during differentiation of THP-1 cell towards macrophage</b> by Li Xing (21105170)

“…The protein-protein interaction (PPI) networks were generated using STRING (H. sapiens; confidence score > 0.7) and visualized in Cytoscape 3.2.1. to elucidate how Aspirin-driven acetylated proteins functionally coordinate within cellular systems. The PPI network was further analyzed to identify densely interconnected functional clusters/modules using topological clustering algorithms. …”

Data_Sheet_1_MEEGIPS—A Modular EEG Investigation and Processing System for Visual and Automated Detection of High Frequency Oscillations.PDF by Peter Höller (461367)

“…<p>High frequency oscillations (HFOs) are electroencephalographic correlates of brain activity detectable in a frequency range above 80 Hz. They co-occur with physiological processes such as saccades, movement execution, and memory formation, but are also related to pathological processes in patients with epilepsy. …”

Population trajectories for synthetic data. by Lorenzo Cappello (11539312)

“…An essential feature of our approach is the ability to track the time-varying association between the populations while making minimal assumptions on their functional shapes via Markov random field priors. We provide nonparametric estimators, extensions of our base model that integrate multiple data sources, and fast scalable inference algorithms. …”

Table 2_Unraveling the role of histone acetylation in sepsis biomarker discovery.docx by Feng Cheng (124653)

“…Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed, followed by machine learning algorithms (LASSO, SVM-RFE, and Boruta) to screen for potential biomarkers. …”

Table 3_Unraveling the role of histone acetylation in sepsis biomarker discovery.docx by Feng Cheng (124653)

“…Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed, followed by machine learning algorithms (LASSO, SVM-RFE, and Boruta) to screen for potential biomarkers. …”

Table 1_Unraveling the role of histone acetylation in sepsis biomarker discovery.docx by Feng Cheng (124653)

“…Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were performed, followed by machine learning algorithms (LASSO, SVM-RFE, and Boruta) to screen for potential biomarkers. …”