Data Sheet 2_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf by Yuwen ShangGuan (22633190)

“…An online risk prediction tool was developed based on the optimized random forest model for real-time individual comorbidity risk calculation.…”

Data Sheet 4_Multidimensional dietary assessment and interpretable machine learning models predict the risk of prediabetes/diabetes and osteoporosis comorbidity in older adults.pdf by Yuwen ShangGuan (22633190)

“…An online risk prediction tool was developed based on the optimized random forest model for real-time individual comorbidity risk calculation.…”

An illustration of the training and testing processes of GeM-LR. by Lin Lin (46073)

“…Then, GeM-LR is estimated by the Expectation-Maximization (EM) algorithm. In particular, the embedded GMM and LR models are jointly optimized by EM iterations. …”

Identify different types of urban renewal implementations at streetscape scale by Xiaotong Wang (20852492)

“…Utilizing a two-stage recognition algorithm, the MTURI model is designed to identify various types of urban renewal activities. …”

Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method by Tao Huang (110613)

“…The XGBoost-based SHAP algorithm not only elucidated the global effects of 17 SNPs across all samples, but also described the interaction between SNPs, providing a visual representation of how SNPs impact the prediction of MetS in an individual. …”

MViT2025 by Linjun He (22524461)

“…Second, a 7×7 dynamic partitioning template together with a boundary compensation algorithm jointly optimizes dense structural representation and resolution adaptability. …”

Supplementary file 2_Patient-specific and interpretable deep brain stimulation optimisation using MRI and clinical review data.xlsx by Apostolos Mikroulis (22473742)

“…Existing electrode contact evaluations can be optionally included in the calculation process for further fine-tuning and adverse effect avoidance.</p>Results<p>Based on a sample of 174 implanted electrode reconstructions from 87 Parkinson’s disease patients, we demonstrate that our algorithm’s DBS parameter settings are more effective in covering the target structure (Wilcoxon p < 5e-13, Hedges’ g > 0.94) and minimising electric field leakage to neighbouring regions (p < 2e-10, g > 0.46) compared to expert parameter settings. …”

Supplementary file 3_Patient-specific and interpretable deep brain stimulation optimisation using MRI and clinical review data.zip by Apostolos Mikroulis (22473742)

“…Existing electrode contact evaluations can be optionally included in the calculation process for further fine-tuning and adverse effect avoidance.</p>Results<p>Based on a sample of 174 implanted electrode reconstructions from 87 Parkinson’s disease patients, we demonstrate that our algorithm’s DBS parameter settings are more effective in covering the target structure (Wilcoxon p < 5e-13, Hedges’ g > 0.94) and minimising electric field leakage to neighbouring regions (p < 2e-10, g > 0.46) compared to expert parameter settings. …”

Supplementary file 1_Patient-specific and interpretable deep brain stimulation optimisation using MRI and clinical review data.docx by Apostolos Mikroulis (22473742)

“…Existing electrode contact evaluations can be optionally included in the calculation process for further fine-tuning and adverse effect avoidance.</p>Results<p>Based on a sample of 174 implanted electrode reconstructions from 87 Parkinson’s disease patients, we demonstrate that our algorithm’s DBS parameter settings are more effective in covering the target structure (Wilcoxon p < 5e-13, Hedges’ g > 0.94) and minimising electric field leakage to neighbouring regions (p < 2e-10, g > 0.46) compared to expert parameter settings. …”

Forest cover and canopy health mapping in Australian subalpine landscape: supervised machine learning models for Sentinel-2 and Landsat images by Weerach Charerntantanakul (21532661)

“…We tested random-forest (RF), support vector machine (SVM), and multiple linear regression (MLR) to find the algorithm that provides the best accuracy. Cross-validation experiments were undertaken to optimize the model configurations. …”

Search for acetylcholinesterase inhibitors by computerized screening of approved drug compounds by T.A. Materova (22770138)

“…The screening process employed the SOL docking program with MMFF94 force field and genetic algorithms for global optimization, targeting the human AChE structure (PDB ID: 6O4W). …”

<b>A virtual tracer experiment to assess the temporal origin of root water uptake, evaporation, and </b><b>drainage</b> by Paolo Nasta (19710883)

“…The PT.m Matlab code determines the drainage transit time based on the particle tracking algorithm, while the VTE.m Matlab code determines the drainage and RWU transit times and relative rainfall contributions to actual evaporation, actual transpiration, and drainage using isotope transport simulations in HYDRUS-1D</a>. …”

Image 1_Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics.jpeg by Zhenze Zhang (22011422)

“…A nomogram was constructed based on key predictors identified through logistic regression.…”

Table 1_Random forest-driven mortality prediction in critical IBD care: a dual-database model integrating comorbidity patterns and real-time physiometrics.docx by Zhenze Zhang (22011422)

“…A nomogram was constructed based on key predictors identified through logistic regression.…”

Image 1_Single-cell and multi-omics analysis reveals the role of stem cells in prognosis and immunotherapy of lung adenocarcinoma patients.tif by Jianan Zheng (9134099)

“…Multiple machine learning algorithms were systematically compared in order to develop an optimal prognostic model. …”

Image 2_Single-cell and multi-omics analysis reveals the role of stem cells in prognosis and immunotherapy of lung adenocarcinoma patients.tif by Jianan Zheng (9134099)

“…Multiple machine learning algorithms were systematically compared in order to develop an optimal prognostic model. …”

NanoDB: Research Activity Data Management System by Lorenci Gjurgjaj (19702207)

“…<p dir="ltr">NanoDB is a Python-based application developed to optimize the management of experimental data in research settings. …”

Table 1_Constructing a screening model to identify patients at high risk of hospital-acquired influenza on admission to hospital.docx by Shangshu Zhang (21097439)

“…Feature screening was performed using least absolute shrinkage and selection operator (LASSO) and the Boruta algorithm. Subsequently eight ML algorithms were applied to analyze and identify the optimal model using a 5-fold cross-validation methodology. …”

Table 2_Constructing a screening model to identify patients at high risk of hospital-acquired influenza on admission to hospital.docx by Shangshu Zhang (21097439)

“…Feature screening was performed using least absolute shrinkage and selection operator (LASSO) and the Boruta algorithm. Subsequently eight ML algorithms were applied to analyze and identify the optimal model using a 5-fold cross-validation methodology. …”

Image 1_Impact of ITH on PRAD patients and feasibility analysis of the positive correlation gene MYLK2 applied to PRAD treatment.tif by Chuanyu Ma (6701108)

“…The ITH-score of PRAD samples was evaluated using the DEPTH algorithm. The optimal cut-off value of RiskScore was calculated based on the difference in survival curves, and PRAD patients were classified into high ITH and low ITH groups based on the optimal cut-off value. …”