DeepB3Pred_source_code by Muhammad Arif (17012472)

“…DeepB3Pred<p dir="ltr">###DeepB3Pred uses the following dependencies:</p><p dir="ltr">MATLAB2018a python 3.10 numpy scipy *pandas scikit-learn catboost= 1.1.1 gc_forset *xgboost-1.5.0 tensorflow=1.15.0 Keras=2.1.6</p><p dir="ltr">###Guiding principles:</p><p dir="ltr">**The data contains training dataset and testing dataset. …”

Accompanying data files (Melbourne, Washington DC, Singapore, and NYC-Manhattan) by Winston Yap (13771969)

“…Each folder contains the training and validation data, graph adjacency information, compiled reported energy consumption data for each city, and generated predictions.</p><p dir="ltr">Each zipped folder consists the following files:</p><ul><li>Graph data - City object nodes (.parquet) and COO format edges (.txt)</li><li>predictions.txt (model predictions from GraphSAGE model)</li><li>final_energy.parquet (Compiled training and validation building energy data)</li></ul><p dir="ltr">The provided files are supplementary to the code repository which provides Python notebooks stepping through the data preprocessing, GNN training, and satellite imagery download processes. …”

AGU24 - EP11D-1300 - Revisiting Megacusp Embayment Occurrence in Monterey Bay and Beyond: High Spatiotemporal Resolution Satellite Imagery Provides New Insight into the Wave Condit... by Kelby Kramer (20706767)

“…D., Vos, K., & Splinter, K. D. (2022). A Python toolkit to monitor sandy shoreline change using high-resolution PlanetScope cubesats. …”

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

“…</p><p dir="ltr"><b>Input:</b></p><ul><li><code>raw_data/glasgow_open_built/glasgow_open_built_areas.shp</code> - Grid defining sampling points</li></ul><p dir="ltr"><b>Command:</b></p><pre><pre>python svi_module/get_svi_data.py<br></pre></pre><p dir="ltr"><b>Output:</b></p><ul><li><code>svi_module/svi_data/svi_info.csv</code> - Image metadata (IDs, coordinates)</li><li><code>svi_module/svi_data/images/</code> - Downloaded street view images</li></ul><h3>Step 2: Predict Perceptions</h3><p dir="ltr">Use pre-trained deep learning models to predict perceptual qualities (safety, beauty, liveliness, etc.) from street view images.…”

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

“…</li><li>The dataframe of extracted colour features from all leaf images and lab variables (ecophysiological predictors and variables to be predicted)</li><li>Set of scripts used for image pre-processing, features extraction, data analytsis, visualization and Machine learning algorithms training, using ImageJ, R and Python.…”

Iterative Methods for Vecchia-Laplace Approximations for Latent Gaussian Process Models by Pascal Kündig (19824557)

“…All methods are implemented in a free C++ software library with high-level Python and R packages. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.…”

Fast, FAIR, and Scalable: Managing Big Data in HPC with Zarr by Alfonso Ladino (21447002)

“…</p><p dir="ltr">In this work, we apply the scientific datacube model to the transformation of large-scale radar datasets from Colombia and the U.S. (NEXRAD), using open-source tools from the Python ecosystem such as Xarray, Xradar, and Dask to enable efficient parallel processing and scalable analysis. …”

PepENS by Abel Chandra (16854753)

“…To the best of our knowledge, this is the first time representations learnt using attention mechanism in transformers are transformed into images to utilize the potential of Convolutional Neural Networks in protein-peptide prediction. …”

TB PROSPECT - Reference-based chemical-genetic interaction profiling to elucidate small molecule mechanism of action in Mycobacterium tuberculosis by Austin Bond (20723383)

“…Code libraries in Matlab (cmapM), Python (cmapPy), and R (cmapR) are publicly available on Github to work with it. …”

<b>Ferritin Degradome Foundation Atlas</b> by Axel Petzold (7076261)

“…</p><p dir="ltr">The resource includes:</p><ul><li>High-resolution degradomes for wild-type ferritin</li><li>CSV tables containing all fragments and calculated properties</li><li>A reproducible Python workflow used to generate the atlas</li><li>A maximally compressed archive containing all outputs</li><li>Full documentation in PDF format</li></ul><p dir="ltr">The Ferritin Degradome Foundation Atlas is intended to support biomarker discovery, neuroimmunology, autoimmune disease research, neurodegenerative disease proteomics, therapeutic target development, autoantibody and cleavage-pattern analyses, and machine-learning applications. …”

MYCroplanters can quantify the interaction between pathogenic and non-pathogenic bacteria and their effects on plant health. by Melissa Y. Chen (11301882)

“…Plants are scanned after 1 week of incubation with bacteria and images are processed using a custom Python script to extract pixel data. …”

IGD-cyberbullying-detection-AI by Bryan James (19921044)

“…IGD and Cyberbullying Detection: A Deep Learning Approach<p dir="ltr">This repository contains the source code and datasets used for the research paper titled <b>"A Novel Machine Learning & Deep Learning Approach Based Dataset Efficacy Study in Predicting Mental Health Outcomes from Internet Gaming Disorder and Cyberbullying"</b>. …”

Code by Baoqiang Chen (21099509)

“…Finally, the output layer produces predictions using a fully connected layer with 64 input neurons and 1 output neuron.…”

Core data by Baoqiang Chen (21099509)

“…Finally, the output layer produces predictions using a fully connected layer with 64 input neurons and 1 output neuron.…”

Genosophus: A Dynamical-Systems Diagnostic Engine for Neural Representation Analysis by Alan Glanz (22109698)

“…</b><code><strong>GenosophusV2.py</strong></code></h3><p dir="ltr">Executable Python implementation of the Genosophus Engine.</p><h3><b>2. …”

Globus Compute: Federated FaaS for Integrated Research Solutions by eRNZ Admin (6438486)

“…HPC enables researchers to perform simulations, modeling, and analysis, which are critical to predicting outcomes, guiding experiments, and developing new technologies [1]. …”

BGC-Prophet by Haohong Zhang (17911673)

“…/output/ --name split --threads 10</code></pre><p dir="ltr"><b>4. Gene Prediction</b></p><p dir="ltr">Detect BGC genes using a trained model:</p><pre><code>bgc_prophet predict --datasetPath .…”

Nucleotide analogue tolerant synthetic RdRp mutant construct for Surveillance and Therapeutic Resistance Monitoring in SARS-CoV-2 by Tahir Bhatti (20961974)

“…</p><p dir="ltr">Using the output of this locally trained AI model a synthetic RdRp region was generated incorporating those predicted mutations and was inserted into the consensus backbone.…”

HMRLBA_V1.0 by Zhenyu Huang (20153082)

“…</p><p dir="ltr"><b>SOTA</b>: Comparative methods used in the contrast experiments.</p><h3><b>Configuration</b></h3><p dir="ltr">It is recommended to use the conda environment (python 3.7), mainly installing the following dependencies:</p><ul><li>​ <b>pytorch (1.9.0)、torch-geometric (2.0.4)、dgl-cu111 (0.6.1)、cudatoolkit (11.1.74)</b></li><li>​ <a href="http://mgltools.scripps.edu/packages/MSMS/" rel="nofollow" target="_blank"><b>msms</b></a><b> </b><b>(2.6.1)、</b><a href="https://swift.cmbi.umcn.nl/gv/dssp/" rel="nofollow" target="_blank"><b>dssp</b></a><b> </b><b>(3.0.0)、</b><a href="https://www.blender.org/" rel="nofollow" target="_blank"><b>blender</b></a><b> </b><b>(3.5.1)、pdb2pqr (2.1.1) 、biopython (1.79)、rdkit (2023.3.1)、transformers (4.24.0)、</b>​ <b>wandb (0.15.4)、pymesh2 (0.3)、pdbfixer (1.6)</b></li></ul><p dir="ltr">See environment.yaml for details.…”

CK4Gen, High Utility Synthetic Survival Datasets by Nicholas Kuo (12369910)

“…<br>[7]: Dispenzieri, et al. “Use of nonclonal serum immunoglobulin free light chains to predict overall survival in the general population”, in <i>Mayo Clinic Proceedings</i>, 2012.…”