Multiple collinearity diagnosis results. by Peiwen Zhang (1863202)

AI-Powered Knowledge Base Enables Transparent Prediction of Nanozyme Multiple Catalytic Activity by Julia Razlivina (18605897)

“…For the first time, we enable the prediction of multiple catalytic activities of nanozymes by training an ensemble learning algorithm achieving <i>R</i>² = 0.75 for the Michaelis–Menten constant and <i>R</i>² = 0.77 for the maximum velocity on unseen test data. …”

Validation of Acetylcholinesterase Inhibition Machine Learning Models for Multiple Species by Patricia A. Vignaux (6911525)

Subjects:

Single-cell based multiple instance learning predicts AML genetic subtypes with high accuracy and identifies clinically relevant cells. by Matthias Hehr (14797745)

Figure 1. TCRD knowledge graphs concept and overview of the meta-path-XGBoost algorithm, MPxgb(AD), and workflow. by Jessica Binder (11748254)

An illustration of the Deepwalk algorithm operation involves a random walk on the graph, resulting in a “walk” sequence, which is then utilized to construct a graph. by Xiaobing Deng (1622233)

IUTF Dataset(Enhanced): Enabling Cross-Border Resource for Analysing the Impact of Rainfall on Urban Transportation Systems by Xuhui Lin (19505503)

“…</p><p dir="ltr"><b>Cross-Continental Diversity</b>: Cities span multiple continents, climate zones, and urban morphologies, enabling robust comparative studies and generalization of findings across different urban contexts.…”

Process of UWB location prediction based on LSTM. by Li Wang (15202)

diaPASEF-Powered Chemoproteomics Enables Deep Kinome Interaction Profiling by Kathryn Woods (4689739)

diaPASEF-Powered Chemoproteomics Enables Deep Kinome Interaction Profiling by Kathryn Woods (4689739)

diaPASEF-Powered Chemoproteomics Enables Deep Kinome Interaction Profiling by Kathryn Woods (4689739)

Global and local feature (predictor variable) explanations from the XGBoost trained with Quebec data (2014–2021). by Hamid Ghanbari (6821096)

Subjects:

Pearson correlations among the candidate predictor variables, calculated at surveillance sites using a 1 000-m radius buffer. by Hamid Ghanbari (6821096)

Subjects:

Performance of ML models for estimating tick population establishment at Quebec surveillance sites from 2022-2023, using predictor variables calculated at the surveillance sites us... by Hamid Ghanbari (6821096)

Subjects:

Candidate predictor variables included in the initial parameter selection. by Hamid Ghanbari (6821096)

Subjects:

Geographic distribution of surveillance sites and environmental context. A) Distribution of tick surveillance sites across Quebec (2014-2023) and Ontario (2015-2018), B) land cover... by Hamid Ghanbari (6821096)

Subjects:

Workflow for estimating the probability of established tick populations. by Hamid Ghanbari (6821096)

Subjects:

List of predictor variables included in the final models, calculated at the surveillance sites using a 1 000-meter radius buffer. by Hamid Ghanbari (6821096)

Subjects:

XGBoost model output for 2022 at 250 m resolution. by Hamid Ghanbari (6821096)

Subjects:

Confusion matrix. by Shaik Ahamed Fayaz (19859670)

“…There are not many studies done on the ML algorithm that enables the prediction of treatment success for patients with pulmonary TB (PTB). …”