Settings for ABC-SMC code. by Román Zapién-Campos (9592478)

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" by FirstName LastName (20554465)

“…</p><p dir="ltr"><i>cd 1point2dem/CIPrediction</i></p><p dir="ltr"><i>python -u point_prediction.py --model [GCN|ChebNet|GATNet]</i></p><h3>step 4: Parallel computation</h3><p dir="ltr">This step uses the trained models to optimize parallel computation. …”

The codes and data for "A Graph Convolutional Neural Network-based Method for Predicting Computational Intensity of Geocomputation" by FirstName LastName (20554465)

“…</p><p dir="ltr"><i>cd 1point2dem/CIPrediction</i></p><p dir="ltr"><i>python -u point_prediction.py --model [GCN|ChebNet|GATNet]</i></p><h3>step 4: Parallel computation</h3><p dir="ltr">This step uses the trained models to optimize parallel computation. …”

Code for the HIVE Appendicitis prediction modelRepository with LLM_data_extractor_optuna for automated feature extraction by Anoeska Schipper (18513465)

“…</p><p dir="ltr"><b>LLM Data Extractor optuna repo</b> is a Python framework for generating and evaluating clinical text predictions using large language models (LLMs) like <code>qwen2.5</code>. …”

Supplementary data for PCI-based pavement modelling: code, presentation, and additional analysis by Azam Amir (21505033)

Code by Baoqiang Chen (21099509)

Analysis Codes for "Gene Signatures Predict Metastatic Organotropism in Breast Cancer: A Machine Learning Analysis of Single-Cell Data" by Ardo Sanjaya (18321300)

OVEP code by Yuanfang Guan (22258765)

“…/usr/bin/env python3</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/plot/plot_auc_nucleotide_length.py` </p><p dir="ltr"> Script related to: plot auc nucleotide length.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/plot/plot_scatter_auc_length.py` </p><p dir="ltr"> Script related to: plot scatter auc length.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel_attention/predict_nucleotide_plot.py` </p><p dir="ltr"> Script related to: predict nucleotide plot.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel_attention/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel_attention/plot_auc_compare_base.py` </p><p dir="ltr"> Script related to: plot auc compare base.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel_attention/plot_auc.py` </p><p dir="ltr"> Script related to: plot auc.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel_1024/predict_nucleotide_plot.py` </p><p dir="ltr"> Script related to: predict nucleotide plot.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel_1024/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel_1024/plot_auc_compare_base.py` </p><p dir="ltr"> Script related to: plot auc compare base.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel_1024/plot_auc.py` </p><p dir="ltr"> Script related to: plot auc.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_nonbinary_train_and_predict_5fold_masklabel/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_nonindelonlyintrain_masklabel/predict_nucleotide_plot.py` </p><p dir="ltr"> Script related to: predict nucleotide plot.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_nonindelonlyintrain_masklabel/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_nonindelonlyintrain_masklabel/plot_auc_compare_base.py` </p><p dir="ltr"> Script related to: plot auc compare base.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_nonindelonlyintrain_indelonlyintest_masklabel/predict_nucleotide_plot.py` </p><p dir="ltr"> Script related to: predict nucleotide plot.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_nonindelonlyintrain_indelonlyintest_masklabel/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_indelonlyonlyintrain_masklabel/predict_nucleotide_plot.py` </p><p dir="ltr"> Script related to: predict nucleotide plot.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_indelonlyonlyintrain_masklabel/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_indelonlyonlyintrain_masklabel/plot_auc_compare_base.py` </p><p dir="ltr"> Script related to: plot auc compare base.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_nonbinary_5fold_masklabel/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel_4096/predict_nucleotide_plot.py` </p><p dir="ltr"> Script related to: predict nucleotide plot.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel_4096/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel_4096/plot_auc_compare_base.py` </p><p dir="ltr"> Script related to: plot auc compare base.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel_4096/plot_auc.py` </p><p dir="ltr"> Script related to: plot auc.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel_512/predict_nucleotide_plot.py` </p><p dir="ltr"> Script related to: predict nucleotide plot.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel_512/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel_512/plot_auc_compare_base.py` </p><p dir="ltr"> Script related to: plot auc compare base.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel_512/plot_auc.py` </p><p dir="ltr"> Script related to: plot auc.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_nonindel_masklabel/predict_nucleotide_plot.py` </p><p dir="ltr"> Script related to: predict nucleotide plot.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_nonindel_masklabel/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_nonindel_masklabel/plot_auc_compare_base.py` </p><p dir="ltr"> Script related to: plot auc compare base.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_nonindel_masklabel/plot_auc.py` </p><p dir="ltr"> Script related to: plot auc.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_indelonlyonlyintrain_nonindelonlyintest_masklabel/predict_nucleotide_plot.py` </p><p dir="ltr"> Script related to: predict nucleotide plot.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_indelonlyonlyintrain_nonindelonlyintest_masklabel/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel/predict_nucleotide_plot.py` </p><p dir="ltr"> Script related to: predict nucleotide plot.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel/plot_auc_compare_base.py` </p><p dir="ltr"> Script related to: plot auc compare base.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_masklabel/plot_auc.py` </p><p dir="ltr"> Script related to: plot auc.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_indelonly_masklabel/predict_nucleotide_plot.py` </p><p dir="ltr"> Script related to: predict nucleotide plot.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_indelonly_masklabel/predict_train.py` </p><p dir="ltr"> Script related to: predict train.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_indelonly_masklabel/plot_auc_compare_base.py` </p><p dir="ltr"> Script related to: plot auc compare base.py</p><p dir="ltr">- `rate_code/code_halftrainmaskmissing_masklabel/base_excludeN_5fold_indelonly_masklabel/plot_auc.py` </p><p dir="ltr"> Script related to: plot auc.py</p><p><br></p><p>---</p><p><br></p><p dir="ltr">## Reproducibility hints</p><p><br></p><p dir="ltr">While this snapshot does not include data/configs, common entry points typically look like:</p><p><br></p><p dir="ltr">- `*predict_train*.py` — training loop (likely 5-fold CV; see corresponding `predict_nucleotide_plot.py` for visualization).…”

MMRCC_original code by Rui Chen (21529631)

6. Motif Code Theory by William Terry (22279591)

“…<p dir="ltr">The Motif Code Theory (MCT) simulation code, mct_unified_code.py, is a Python 3.9 script that models the universe as a time-dependent directed multigraph G(t) = (V(t), E(t)) with N=10^7 vertices (representing quantum fields/particles) and edges (interactions). …”

DeepB3Pred - code by Saleh Musleh (22263718)

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 .…”

DeepB3Pred_source_code by Muhammad Arif (17012472)

Computing speed and memory usage. by David Berling (22170661)

“…<b>(b)</b> Physical memory consumption depending on simulated plane in radial and depth direction. Color coding same as in (a). Memory consumption was recorded as the maximum resident size during simulation monitored with the Python built-in module resource. …”

The codes and data for "Lane Extraction from Trajectories at Road Intersections Based on Graph Transformer Network" by Chongshan Wan (19247614)

“…The lane extraction result is saved in `result/predicted_lane.csv`.</p><p dir="ltr"></p><h2>Requirements</h2><p dir="ltr">The codes use the following dependencies with Python 3.11</p><ul><li>networkx==3.2.1</li><li>pytorch==2.0.1</li><li>torch-geometric==2.5.3</li><li>geopandas==1.0.1</li></ul><p dir="ltr"><br></p>…”

Schematic architecture of OmnibusX, illustrating its modular design and data flow. by Linh Truong (4669021)

<b>Use case codes of the DDS3 and DDS4 datasets for bacillus segmentation and tuberculosis diagnosis, respectively</b> by Marly G F Costa (19812192)

“…<p dir="ltr"><b>Use case codes of the DDS3 and DDS4 datasets for bacillus segmentation and tuberculosis diagnosis, respectively</b></p><p dir="ltr">The code was developed in the Google Collaboratory environment, using Python version 3.7.13, with TensorFlow 2.8.2. …”

Data and some code used in the paper:<b>Expansion quantization network: A micro-emotion detection and annotation framework</b> by Zhou (20184816)

“…Attached is the micro-emotion annotation code based on pytorch, which can be used to annotate the Goemotions dataset by yourself, or predict the emotion classification based on the annotation results. …”

The Twin Prime Conjecture: Collapse-Band Parity Cancellation and Operator Proof by Jennifer Bulyaki (22283308)

Data and Code for 'A Comparative Study of Physics-Informed and Data-Driven Neural Networks for Compound Flood Simulation at River-Ocean Interfaces: A Case Study of Hurricane Irene' by Dongyu Feng (21196556)

“…<br><br>conda create --name tf2 --file requirement_tf2.txt<br>conda activate tf2<br><br><br>### Before training<br>Before running the code, need to create folders to save the model output<br><br>For CNN, create /files/CNN<br><br>For PINNs, create /saved_model<br><br>For saving figures from visualization, create /figures<br><br></p><p dir="ltr">Training and Results</p><p dir="ltr"><br>PINNs<br>Training: To train the model, run:</p><p dir="ltr">python PINN_test_bnd_uh_Telemac.py</p><p dir="ltr">python PINN_test_bnd_uh_Telemac_FDM.py<br></p><p dir="ltr">Result Plotting and Comparison: For plotting and comparing results, use:</p><p dir="ltr">python PINN_plot_comparison.py<br><br><br>Data-driven Model<br>CNN Training: To train the CNN model, execute:</p><p dir="ltr">python train_CNN.py<br><br>Result Visualization: To visualize the results of the CNN model, run:</p><p dir="ltr">python predict_CNN.py<br><br>To reproduce all results and figures in the manuscript, please refer to the scripts in analysis/</p>…”