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. …”

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>Here, we present an open-access Python code including the ML model for inference to facilitate prospective utilization of the developed model and further study of the nuMoM2b and similar datasets with machine learning approaches.…”

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><p dir="ltr"><b>Features</b>:</p><ul><li><b>User-Friendly GUI</b>: For users unfamiliar with Python, we've developed a graphical user interface (GUI) that enables predictions without the need to install or run Python code.…”

Replication Package: Quantum vs. Classical Machine Learning Algorithms for Software Defect Prediction by Md Nadim (17811941)

Estimating dopamine effects on humans. by Domenico Guarino (20428000)

Striatal neurons sharpen and reverse their differences. by Domenico Guarino (20428000)

Oka et al., Supplementary Data for "Development of a battery emulator using deep learning model to predict the charge–discharge voltage profile of lithium-ion batteries" by Kanato Oka (20132185)

“…(<b>DOI:</b> 10.1038/s41598-024-80371-9 )<br><br>A zip file contains following data and codes.</p><p dir="ltr"><br></p><p dir="ltr">01_lstm_model_making.py</p><p dir="ltr">This file is a Python script for reading battery charge-discharge data and training an LSTM model. …”

Western Oregon Wet Dry (WOWTDR) annual predictions of late summer streamflow status for western Oregon, 2019-2021 by Jonathan D. Burnett (19659601)

“…Also included is all R code and Python code needed to run and process this model.…”

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

“…The implementation is freely available at https://github.com/PiyachatU/ParaDeep, with Python (PyTorch) code and a Google Colab interface for ease of use.…”

Pythonproject-HCC by Roy Zhao (22503761)

Flowchart of the study participants. by Saeid Rasouli (20370998)

“…<div><p>Background</p><p>Optic neuritis (ON) can be an initial clinical presentation of multiple sclerosis This study aims to provide a practical predictive model for identifying at-risk ON patients in developing MS.…”

Feature importance of variables. by Saeid Rasouli (20370998)

“…<div><p>Background</p><p>Optic neuritis (ON) can be an initial clinical presentation of multiple sclerosis This study aims to provide a practical predictive model for identifying at-risk ON patients in developing MS.…”

Annual rainfall map of Jiangxi Province. by Qun Zhao (603175)

Distance from target watershed (Shi Shi as the example) to all selected stations. by Qun Zhao (603175)

Vegetation coverage over the surrounding area of Jiangxi Province. by Qun Zhao (603175)

Selected source basins used in the transfer learning (in red triangles) after filtering out stations with missing data. by Qun Zhao (603175)

Digital elevation map of Jiangxi Province. by Qun Zhao (603175)

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