Image 10_Integrated bioinformatic analysis and machine learning developed a prognostic model based on mitochondrial function for acute myeloid leukemia.jpeg حسب Xingbiao Chen (22481431)

"…A total of fourteen machine learning algorithms were employed, yielding 148 unique combinations. …"

Image 3_Integrated bioinformatic analysis and machine learning developed a prognostic model based on mitochondrial function for acute myeloid leukemia.jpeg حسب Xingbiao Chen (22481431)

"…A total of fourteen machine learning algorithms were employed, yielding 148 unique combinations. …"

Image 6_Integrated bioinformatic analysis and machine learning developed a prognostic model based on mitochondrial function for acute myeloid leukemia.jpeg حسب Xingbiao Chen (22481431)

"…A total of fourteen machine learning algorithms were employed, yielding 148 unique combinations. …"

Image 9_Integrated bioinformatic analysis and machine learning developed a prognostic model based on mitochondrial function for acute myeloid leukemia.jpeg حسب Xingbiao Chen (22481431)

"…A total of fourteen machine learning algorithms were employed, yielding 148 unique combinations. …"

DATASET AI حسب Elena Stamate (18836305)

"…Performance metrics include accuracy, precision, recall, F1-score, and Matthews Correlation Coefficient (MCC).</p><p dir="ltr">All data have been de-identified and processed in accordance with institutional ethical standards.…"

Image 5_Integrated bioinformatic analysis and machine learning developed a prognostic model based on mitochondrial function for acute myeloid leukemia.jpeg حسب Xingbiao Chen (22481431)

"…A total of fourteen machine learning algorithms were employed, yielding 148 unique combinations. …"

Image 4_Integrated bioinformatic analysis and machine learning developed a prognostic model based on mitochondrial function for acute myeloid leukemia.jpeg حسب Xingbiao Chen (22481431)

"…A total of fourteen machine learning algorithms were employed, yielding 148 unique combinations. …"

Table 1_Integrated bioinformatic analysis and machine learning developed a prognostic model based on mitochondrial function for acute myeloid leukemia.xls حسب Xingbiao Chen (22481431)

"…A total of fourteen machine learning algorithms were employed, yielding 148 unique combinations. …"

Image 2_Integrated bioinformatic analysis and machine learning developed a prognostic model based on mitochondrial function for acute myeloid leukemia.jpeg حسب Xingbiao Chen (22481431)

"…A total of fourteen machine learning algorithms were employed, yielding 148 unique combinations. …"

An open-pit mine segmentation dataset for deep learning حسب Lin Gang (18600526)

"…It was developed through a systematic process. Firstly, by conducting comprehensive literature research, the Point of Interest (POI) data of open-pit mines was summarized. …"

Uncertainty-Aware Deep Learning and Structural Feature Analysis for Reliable Nephrotoxicity Prediction حسب Jian-Wang Liu (22085230)

"…Using this dataset, we developed classification models employing both traditional machine learning algorithms and graph-based deep learning methods. Our results demonstrate that the Directed Message Passing Neural Network model, combined with molecular graphs and ChemoPy2D descriptors, outperformed other models, achieving a mean Kappa value of 70.3%. …"

Data Sheet 2_Impact of climate change on the potential global prevalence of Macrophomina phaseolina (Tassi) Goid. under several climatological scenarios.csv حسب Peter F. Farag (21097466)

"…</p>Results<p>Based on the Jackknife test, minimum temperature of coldest month (bio_6) represented the most effective bioclimatological parameter to fungus distribution with a 52.5% contribution. …"

Data Sheet 1_Impact of climate change on the potential global prevalence of Macrophomina phaseolina (Tassi) Goid. under several climatological scenarios.zip حسب Peter F. Farag (21097466)

"…</p>Results<p>Based on the Jackknife test, minimum temperature of coldest month (bio_6) represented the most effective bioclimatological parameter to fungus distribution with a 52.5% contribution. …"

Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study حسب Zheng Zhang (88857)

"…We applied four ML algorithms—Extreme Gradient Boosting (XGBoost), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Logistic Regression (LR)—to classify patients with or without HIBPV. …"

Optimizing critical quality attributes of fast disintegrating tablets using artificial neural networks: a scientific benchmark study حسب Jagruti Desai (20400468)

"…<p>The objective of this study is to create predictive models utilizing machine learning algorithms, including Artificial Neural Networks (ANN), k-nearest neighbor (kNN), support vector machines (SVM), and linear regression, to predict critical quality attributes (CQAs) such as hardness, friability, and disintegration time of fast disintegrating tablets (FDTs).…"

Image 3_Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression.tif حسب Kun Guo (283211)

"…The SVM model demonstrated exceptional performance, with an AUC of 0.9453 on the training set and 0.9213 on the validation set. A Nomogram based on Logistic Regression was developed for visualizing prognostic risk, incorporating factors such as the National Institutes of Health Stroke Scale (NIHSS) score, Barthel Index (BI), Watanabe Drinking Test (KWST) score, Platelet Distribution Width (PDW), and others. …"

Image 2_Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression.tif حسب Kun Guo (283211)

"…The SVM model demonstrated exceptional performance, with an AUC of 0.9453 on the training set and 0.9213 on the validation set. A Nomogram based on Logistic Regression was developed for visualizing prognostic risk, incorporating factors such as the National Institutes of Health Stroke Scale (NIHSS) score, Barthel Index (BI), Watanabe Drinking Test (KWST) score, Platelet Distribution Width (PDW), and others. …"

Data Sheet 1_Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression.zip حسب Kun Guo (283211)

"…The SVM model demonstrated exceptional performance, with an AUC of 0.9453 on the training set and 0.9213 on the validation set. A Nomogram based on Logistic Regression was developed for visualizing prognostic risk, incorporating factors such as the National Institutes of Health Stroke Scale (NIHSS) score, Barthel Index (BI), Watanabe Drinking Test (KWST) score, Platelet Distribution Width (PDW), and others. …"

Image 1_Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression.tif حسب Kun Guo (283211)

"…The SVM model demonstrated exceptional performance, with an AUC of 0.9453 on the training set and 0.9213 on the validation set. A Nomogram based on Logistic Regression was developed for visualizing prognostic risk, incorporating factors such as the National Institutes of Health Stroke Scale (NIHSS) score, Barthel Index (BI), Watanabe Drinking Test (KWST) score, Platelet Distribution Width (PDW), and others. …"

Table 2_Applying machine learning techniques to predict the risk of distant metastasis from gastric cancer: a real world retrospective study.docx حسب Xinxin Qin (14280806)

"…We applied six machine learning algorithms to construct a model that can predict distant metastasis of gastric cancer. …"