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. …”

Image 2_Unraveling the role of histone acetylation in sepsis biomarker discovery.tif 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 4_Unraveling the role of histone acetylation in sepsis biomarker discovery.xlsx 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. …”

Image 1_Unraveling the role of histone acetylation in sepsis biomarker discovery.tif 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 5_Unraveling the role of histone acetylation in sepsis biomarker discovery.csv 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. …”

Data Sheet 1_Identification of key ferroptosis-related genes and therapeutic target in nasopharyngeal carcinoma.zip by Yuanyuan Gu (3813658)

“…Four machine learning algorithms screened hub genes, validated by ROC curves. …”

Table 1_Machine learning identifies PYGM as a macrophage polarization–linked metabolic biomarker in rectal cancer prognosis.docx by Chengyuan Xu (4174936)

“…</p>Methods<p>We constructed a macrophage polarization gene signature (MPGS) by integrating weighted gene co-expression network analysis (WGCNA) with multiple machine learning algorithms across two independent cohorts: 363 rectal cancer samples from GSE87211 and 177 samples from The Cancer Genome Atlas (TCGA). …”

Image 2_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

“…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …”

Image 3_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

“…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …”

Image 1_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

“…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …”

Table 2_Multi-omics dissection of fatty acid metabolism heterogeneity identifies PRDX1 as a prognostic marker in bladder cancer.xlsx by Li Wang (15202)

“…Cross−platform scoring and co−expression analysis produced a refined high−FAM gene set. …”

Image 4_Leveraging the integration of bioinformatics and machine learning to uncover common biomarkers and molecular pathways underlying diabetes and nephrolithiasis.tif by Xudong Shen (205653)

“…Differentially expressed genes (DEGs) were subsequently analyzed using 10 commonly used machine learning algorithms, generating 101 unique combinations to identify the final DEGs. …”

Integrative analysis of mitochondrial and immune pathways in diabetic kidney disease: identification of AASS and CASP3 as key predictors and therapeutic targets by Xinxin Yu (528120)

“…Machine learning algorithms were employed to prioritize key biomarkers for further investigation. …”