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

Submit to AGU-Manuscript-Enhancing Landslide Displacement Prediction Using a Spatio-Temporal Deep Learning Model with Interpretable Features by Jia Wang (20526992)

“…It includes the monitoring data and model prediction results in two Excel files, along with the corresponding Python code used in the study. …”

A model of neural population dynamics for flexible sensorimotor control by Hari Kalidindi (17123467)

Supporting data for "Prediction and Classification of Bacterial Virulence Factors using Deep Learning" by Wai Kai Tsui (17853245)

“…The source files contain raw data (i.e., VF and non-VF sequences), processed data, and the model training and validation scripts. The trained model is provided in a compiled standalone python package called DeepVIC. …”

Table 3_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.docx by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Table 5_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.docx by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Table 1_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.docx by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Table 4_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.docx by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Table 2_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.docx by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Image 3_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.tiff by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Image 2_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.tiff by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Image 4_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.tiff by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

Image 1_Risk prediction model for precancerous gastric lesions based on magnifying endoscopy combined with narrow-band imaging features.tiff by Jingna Tao (20997176)

“…Variables showing statistical significance underwent collinearity diagnosis before model inclusion. We constructed predictive models using Bayesian stepwise discrimination, random forest, and XGBoost algorithms. …”

SingleFrag by Maribel Pérez Ribera (19868316)

“…</li><li><ol><li><b>ANN</b>: Artificial Neural Networks</li><li><b>GNN</b>: Graph Neural Networks</li><li><b>COM</b>: Networks that combine the predictive power of ANN and GNN</li></ol></li><li><b>Mol2vecModel</b>: Contains a Mol2vec model trained to obtain a 300-dimensional vector from molecule SMILES.…”

Supplementary file 1_ParaDeep: sequence-based deep learning for residue-level paratope prediction using chain-aware BiLSTM-CNN models.docx by Piyachat Udomwong (22563212)

“…On an independent blind test set, ParaDeep attained F1 = 0.723 and MCC = 0.685 for H chains, and F1 = 0.607 and MCC = 0.587 for L chains, representing a 27% MCC improvement over the sequence-based baseline Parapred. Chain-specific modeling revealed that heavy chains provide stronger sequence-based predictive signals, while light chains benefit more from structural context. …”

ML model for prediction of postpartum rehospitalization in pregnant women/new mothers using readily obtainable pre-pregnancy or early pregnancy sociodemographic and health determin... by Martin Frasch (5754731)

“…</li><li>No open-access machine learning (ML) models of risk prediction for re-hospitalization of pregnant women exist.…”

Comparison of the results obtained combining NeKo and Druglogics pipeline with the original publication from [28]. by Marco Ruscone (11747264)

Liang et al., 2024_CEE_BrGMM_BAE: A Clustering Model for Predicting Freshwater and Halo-Alkaliphilic Bacterial Assemblages Using brGDGTs by Jie Liang (14213144)

“…<p dir="ltr">BrGMM_BAE is a specialized clustering model designed to predict bacterial assemblages in freshwater or halo-alkaline environments. …”

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. …”