Data for "A multi-objective platform for autonomous property targeting and optimization of colloidal lead halide perovskite quantum dots" حسب Neal Munyebvu (19690954)

List of palmitoylation-related genes. حسب Qijun Wo (12992781)

"…A diagnostic model based on these genes achieved an AUC of 0.929, demonstrating robust accuracy in distinguishing PCa from normal tissues. Functional analysis revealed roles in lipid metabolism and immune modulation, with ssGSEA highlighting correlations between key genes and immune cell infiltration. …"

Enrichment analysis of mRNA-RBP. حسب Qijun Wo (12992781)

"…A diagnostic model based on these genes achieved an AUC of 0.929, demonstrating robust accuracy in distinguishing PCa from normal tissues. Functional analysis revealed roles in lipid metabolism and immune modulation, with ssGSEA highlighting correlations between key genes and immune cell infiltration. …"

Enrichment analysis of drugs and target genes. حسب Qijun Wo (12992781)

"…A diagnostic model based on these genes achieved an AUC of 0.929, demonstrating robust accuracy in distinguishing PCa from normal tissues. Functional analysis revealed roles in lipid metabolism and immune modulation, with ssGSEA highlighting correlations between key genes and immune cell infiltration. …"

Hippocampal and cortical activity reflect early hyperexcitability in an Alzheimer's mouse model حسب Marina Diachenko (19739092)

"…</p><p dir="ltr">All data are available upon request. The standalone Python implementation of the fE/I algorithm is available under a CC-BY-NC-SA license at <a href="https://github.com/arthur-ervin/crosci" target="_blank">https://github.com/arthur-ervin/crosci</a>. …"

Association between the multiscale structural principal gradient and morphometric features. حسب Yirong He (11277990)

Further analysis of AR/cuproptosis-related genes and drug interaction. حسب Ting Yi (2855855)

Analysis of <i>dpy-27::AID</i> RNA-seq samples. حسب Jessica Trombley (21030070)

RNA-seq analysis of <i>cec</i>-4 and <i>dpy</i>-21 mutants. حسب Jessica Trombley (21030070)

S1 Graphical abstract - حسب José M. Rivera-Arbeláez (12418512)

"…<div><p>Engineered heart tissues (EHTs) have shown great potential in recapitulating tissue organization, functions, and cell-cell interactions of the human heart <i>in vitro</i>. …"

<b>AI for imaging plant stress in invasive species </b>(dataset from the article https://doi.org/10.1093/aob/mcaf043) حسب Erola Fenollosa (20977421)

"…<p dir="ltr">This dataset contains the data used in the article <a href="https://academic.oup.com/aob/advance-article/doi/10.1093/aob/mcaf043/8074229" rel="noreferrer" target="_blank">"Machine Learning and digital Imaging for Spatiotemporal Monitoring of Stress Dynamics in the clonal plant Carpobrotus edulis: Uncovering a Functional Mosaic</a>", which includes the complete set of collected leaf images, image features (predictors) and response variables used to train machine learning regression algorithms.…"

Comparative Clustering Methods. حسب Matthew R. Baker (436704)

<b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> حسب 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.…"

Table 4_Integrated cytomembrane proteomics identifies EpCAM/MGST1 as therapeutic targets in metastatic laryngeal carcinoma.xlsx حسب Suling Zhuang (21775925)

"…Potential immunotherapy targets were identified and prioritized using an in silico screening algorithm for cytomembrane proteome.</p>Results<p>The in silico screening algorithm for cytomembrane proteome led to the recognition of EpCAM and MGST1 as potential targets. …"

Table 5_Integrated cytomembrane proteomics identifies EpCAM/MGST1 as therapeutic targets in metastatic laryngeal carcinoma.xlsx حسب Suling Zhuang (21775925)

"…Potential immunotherapy targets were identified and prioritized using an in silico screening algorithm for cytomembrane proteome.</p>Results<p>The in silico screening algorithm for cytomembrane proteome led to the recognition of EpCAM and MGST1 as potential targets. …"

Table 2_Integrated cytomembrane proteomics identifies EpCAM/MGST1 as therapeutic targets in metastatic laryngeal carcinoma.docx حسب Suling Zhuang (21775925)

"…Potential immunotherapy targets were identified and prioritized using an in silico screening algorithm for cytomembrane proteome.</p>Results<p>The in silico screening algorithm for cytomembrane proteome led to the recognition of EpCAM and MGST1 as potential targets. …"

Table 6_Integrated cytomembrane proteomics identifies EpCAM/MGST1 as therapeutic targets in metastatic laryngeal carcinoma.xlsx حسب Suling Zhuang (21775925)

"…Potential immunotherapy targets were identified and prioritized using an in silico screening algorithm for cytomembrane proteome.</p>Results<p>The in silico screening algorithm for cytomembrane proteome led to the recognition of EpCAM and MGST1 as potential targets. …"

Table 1_Integrated cytomembrane proteomics identifies EpCAM/MGST1 as therapeutic targets in metastatic laryngeal carcinoma.docx حسب Suling Zhuang (21775925)

"…Potential immunotherapy targets were identified and prioritized using an in silico screening algorithm for cytomembrane proteome.</p>Results<p>The in silico screening algorithm for cytomembrane proteome led to the recognition of EpCAM and MGST1 as potential targets. …"

Table 3_Integrated cytomembrane proteomics identifies EpCAM/MGST1 as therapeutic targets in metastatic laryngeal carcinoma.xlsx حسب Suling Zhuang (21775925)

"…Potential immunotherapy targets were identified and prioritized using an in silico screening algorithm for cytomembrane proteome.</p>Results<p>The in silico screening algorithm for cytomembrane proteome led to the recognition of EpCAM and MGST1 as potential targets. …"

NanoDB: Research Activity Data Management System حسب Lorenci Gjurgjaj (19702207)

"…Cross-Platform Compatibility: Works on Windows, macOS, and Linux. In a Python environment or as an executable. Ease of Implementation: Using the flexibility of the Python framework all the data setup and algorithm can me modified and new functions can be easily added. …"