Image 1_Integrated transcriptomic and network analysis reveals candidate immune–metabolic biomarkers in children with the inattentive type of ADHD.tif by Qiaoyan Shao (22357861)

“…High-confidence biomarkers were selected via a multi-step pipeline combining protein-protein interaction (PPI) network analysis and machine learning feature selection (LASSO regression, Boruta algorithm). …”

Table 2_Integrated transcriptomic and network analysis reveals candidate immune–metabolic biomarkers in children with the inattentive type of ADHD.xlsx by Qiaoyan Shao (22357861)

“…High-confidence biomarkers were selected via a multi-step pipeline combining protein-protein interaction (PPI) network analysis and machine learning feature selection (LASSO regression, Boruta algorithm). …”

Table 1_Leveraging a disulfidptosis-based signature to characterize heterogeneity and optimize treatment in multiple myeloma.docx by Bingxin Zhang (13011397)

“…We further explored genetic mutation mapping, protein-protein interactions, functional enrichment, drug sensitivity, drug prediction, and immune infiltration analysis among subtypes and risk subgroups. …”

DataSheet1_Identification and validation of efferocytosis-related biomarkers for the diagnosis of metabolic dysfunction-associated steatohepatitis based on bioinformatics analysis... by Chenghui Cao (12472029)

“…This analysis was followed by a series of in-depth investigations, including protein–protein interaction (PPI), correlation analysis, and functional enrichment analysis, to uncover the molecular interactions and pathways at play. …”

Data Sheet 1_Multiple analytical perspectives of mitochondrial genes in the context of preeclampsia: potential diagnostic markers.docx by Can Li (31070)

“…Data from three datasets were integrated using the ComBat algorithm to mitigate batch effects. Differential expression analysis identified genes differentially expressed between PE cases and Control group. …”

Data Sheet 1_Deciphering macrophage differentiation and cell death dynamics in heart failure: a single-cell sequencing odyssey.zip by Jin Wei (424761)

“…Integrated differential expression analysis, protein–protein interaction network mapping, and multi-algorithm feature selection (LASSO, SVM-RFE, Random Forest) were performed, and candidate biomarkers were validated using an independent bulk RNA-seq dataset (GSE57345).…”

Table 1_Identifying pyroptosis-hub genes and immune infiltration in neonatal hypoxic-ischemic brain injury.docx by Chi Qin (10001651)

“…Immune infiltration analysis revealed that, compared to the control group, the HIBD group exhibited higher levels of innate immune cells (e.g., macrophages, M0 cells, and dendritic cells) and adaptive immune cells (e.g., CD8 naïve T cells, CD4 follicular helper T cells, and Th1 cells). The ssGSEA algorithm results indicated differences in 25 types of immune cells and 10 immune functions. …”

Data Sheet 2_Deciphering macrophage differentiation and cell death dynamics in heart failure: a single-cell sequencing odyssey.zip by Jin Wei (424761)

“…Integrated differential expression analysis, protein–protein interaction network mapping, and multi-algorithm feature selection (LASSO, SVM-RFE, Random Forest) were performed, and candidate biomarkers were validated using an independent bulk RNA-seq dataset (GSE57345).…”

Image 1_Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications.tif by Can Qi (540350)

“…Univariate Cox analysis and the LASSO algorithm were used to identify biomarkers for prognosis. …”

Table 3_Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications.xlsx by Can Qi (540350)

“…Univariate Cox analysis and the LASSO algorithm were used to identify biomarkers for prognosis. …”

Supplementary file 1_Identifying pyroptosis-hub genes and immune infiltration in neonatal hypoxic-ischemic brain injury.docx by Chi Qin (10001651)

“…Immune infiltration analysis revealed that, compared to the control group, the HIBD group exhibited higher levels of innate immune cells (e.g., macrophages, M0 cells, and dendritic cells) and adaptive immune cells (e.g., CD8 naïve T cells, CD4 follicular helper T cells, and Th1 cells). The ssGSEA algorithm results indicated differences in 25 types of immune cells and 10 immune functions. …”

Table 1_Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications.xlsx by Can Qi (540350)

“…Univariate Cox analysis and the LASSO algorithm were used to identify biomarkers for prognosis. …”

Table 2_Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications.xlsx by Can Qi (540350)

“…Univariate Cox analysis and the LASSO algorithm were used to identify biomarkers for prognosis. …”

Landscape17 by Vlad Carare (22092515)

“…</p><p dir="ltr">We utilized TopSearch, an open-source Python package, to perform landscape exploration, at an estimated cost of 10⁵CPUh. …”

<b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> by Shubham Pawar (22471285)

“…</p><h2>Project Structure</h2><pre><pre>Perception_based_neighbourhoods/<br>├── raw_data/<br>│ ├── ET_cells_glasgow/ # Glasgow grid cells for analysis<br>│ └── glasgow_open_built/ # Built area boundaries<br>├── svi_module/ # Street View Image processing<br>│ ├── svi_data/<br>│ │ ├── svi_info.csv # Image metadata (output)<br>│ │ └── images/ # Downloaded images (output)<br>│ ├── get_svi_data.py # Download street view images<br>│ └── trueskill_score.py # Generate TrueSkill scores<br>├── perception_module/ # Perception prediction<br>│ ├── output_data/<br>│ │ └── glasgow_perception.nc # Perception scores (demo data)<br>│ ├── trained_models/ # Pre-trained models<br>│ ├── pred.py # Predict perceptions from images<br>│ └── readme.md # Training instructions<br>└── cluster_module/ # Neighbourhood clustering<br> ├── output_data/<br> │ └── clusters.shp # Final neighbourhood boundaries<br> └── cluster_perceptions.py # Clustering algorithm<br></pre></pre><h2>Prerequisites</h2><ul><li>Python 3.8 or higher</li><li>GDAL/OGR libraries (for geospatial processing)</li></ul><h2>Installation</h2><ol><li>Clone this repository:</li></ol><p dir="ltr">Download the zip file</p><pre><pre>cd perception_based_neighbourhoods<br></pre></pre><ol><li>Install required dependencies:</li></ol><pre><pre>pip install -r requirements.txt<br></pre></pre><p dir="ltr">Required libraries include:</p><ul><li>geopandas</li><li>pandas</li><li>numpy</li><li>xarray</li><li>scikit-learn</li><li>matplotlib</li><li>torch (PyTorch)</li><li>efficientnet-pytorch</li></ul><h2>Usage Guide</h2><h3>Step 1: Download Street View Images</h3><p dir="ltr">Download street view images based on the Glasgow grid sampling locations.…”

Image 3_The osteosarcoma immune microenvironment in progression: PLEK as a prognostic biomarker and therapeutic target.tif by Yunpeng Zou (3723007)

“…</p>Methods<p>We conducted differential expression and survival analyses using OS transcriptomic datasets and TCGA/GTEx data. Protein–protein interaction networks, GO/KEGG enrichment, and CytoHubba algorithms identified core hub genes. …”

Image 2_The osteosarcoma immune microenvironment in progression: PLEK as a prognostic biomarker and therapeutic target.tif by Yunpeng Zou (3723007)

“…</p>Methods<p>We conducted differential expression and survival analyses using OS transcriptomic datasets and TCGA/GTEx data. Protein–protein interaction networks, GO/KEGG enrichment, and CytoHubba algorithms identified core hub genes. …”

Image 4_The osteosarcoma immune microenvironment in progression: PLEK as a prognostic biomarker and therapeutic target.tif by Yunpeng Zou (3723007)

“…</p>Methods<p>We conducted differential expression and survival analyses using OS transcriptomic datasets and TCGA/GTEx data. Protein–protein interaction networks, GO/KEGG enrichment, and CytoHubba algorithms identified core hub genes. …”

Image 5_The osteosarcoma immune microenvironment in progression: PLEK as a prognostic biomarker and therapeutic target.tif by Yunpeng Zou (3723007)

“…</p>Methods<p>We conducted differential expression and survival analyses using OS transcriptomic datasets and TCGA/GTEx data. Protein–protein interaction networks, GO/KEGG enrichment, and CytoHubba algorithms identified core hub genes. …”

Image 1_The osteosarcoma immune microenvironment in progression: PLEK as a prognostic biomarker and therapeutic target.tif by Yunpeng Zou (3723007)

“…</p>Methods<p>We conducted differential expression and survival analyses using OS transcriptomic datasets and TCGA/GTEx data. Protein–protein interaction networks, GO/KEGG enrichment, and CytoHubba algorithms identified core hub genes. …”