An optional formula for calculating crustal thickness using Sr/Y ratio and its application to the southeastern margin of the Central Asian Orogenic Belt حسب Xu Ma (45624)

"…Global lower crust-derived magmatic rock (younger than 23 Ma) data from diverse geological settings were selected and filtered using key geochemical criteria, alternating and iteratively using K-means clustering algorithm and improved Thompson tau method to exclude outliers. …"

Table 1_Identification and validation of prognostic genes associated with T-cell exhaustion and macrophage polarization in breast cancer.xlsx حسب Fengqiang Cui (21429032)

"…</p>Results<p>According to random survival forest (RSF) algorithm (the best combination with the greatest C-index of 0.799), 7 prognostic genes were selected, which are PGK1, BTG2, TANK, CFB, EIF4E3, TNFRSF18, and BATF. …"

Table 1_Machine learning-based prognostic prediction model of pneumonia-associated acute respiratory distress syndrome.xlsx حسب Jing Lv (128179)

"…All patients’ clinical data were first results within 24 h of admission. 20% of the total samples were randomly selected as the test set, and the remaining samples were used as the training set for crossvalidation, and six different models were constructed, including Logistic Regression, Random Forest, NaiveBayes, SVM, XGBoost and Adaboost. …"

Table 2_Identification and validation of prognostic genes associated with T-cell exhaustion and macrophage polarization in breast cancer.xlsx حسب Fengqiang Cui (21429032)

"…</p>Results<p>According to random survival forest (RSF) algorithm (the best combination with the greatest C-index of 0.799), 7 prognostic genes were selected, which are PGK1, BTG2, TANK, CFB, EIF4E3, TNFRSF18, and BATF. …"

VTE Risk Assessment Tool. حسب Youli Jiang (15249070)

"…</p><p> Methods </p><p>We developed a machine learning model using clinical data from patients with acute ischemic stroke (AIS) admitted between December 2021 and December 2023. …"

The predictive performance on test set. حسب Youli Jiang (15249070)

"…</p><p> Methods </p><p>We developed a machine learning model using clinical data from patients with acute ischemic stroke (AIS) admitted between December 2021 and December 2023. …"

Image 1_Potential biomarkers and immune infiltration linking endometriosis with recurrent pregnancy loss based on bioinformatics and machine learning.pdf حسب Jianhui Chen (1362585)

"…Protein-protein interaction (PPI) network and two machine learning algorithms were applied to identify the common core genes in both diseases. …"

Landscape17 حسب Vlad Carare (22092515)

"…We validated the convergence, grid, and spin settings against published data from rMD17, using the appropriate functional and basis set: PBE/def2-SVP. …"

Image 1_Machine learning-driven exploration of therapeutic targets for atrial fibrillation-joint analysis of single-cell and bulk transcriptomes and experimental validation.tif حسب Yicheng Wang (810922)

"…Three machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine—Recursive Feature Elimination (SVM-RFE), and random forest (RF), were applied to screen key genes related to AF. …"

Image 2_Machine learning-driven exploration of therapeutic targets for atrial fibrillation-joint analysis of single-cell and bulk transcriptomes and experimental validation.tif حسب Yicheng Wang (810922)

"…Three machine learning algorithms, Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine—Recursive Feature Elimination (SVM-RFE), and random forest (RF), were applied to screen key genes related to AF. …"

FC-Duplex IgG1 (HTLV-1/2) accuracy for differential diagnosis of HTLV infections. حسب Júlia Pereira Martins (12415351)

"…<b>(B)</b> Decision tree algorithm was built using pre-selected set of attributes applied for differential diagnosis according to the following criteria: Criterion 1 – Delta [α-MT-2 (1:32) – α-MoT (1:32)] & cut-off limit of PPFC = -2; Criterion 2 – Delta [α-MT-2 (1:32) – α-MoT (1:1,024)] & cut-off limit of PPFC = 25 and Criterion 3 – Delta [α-MT-2 (1:32) – α-MoT (1:2,048)] & cut-off limit of PPFC = 35. …"

Table 2_L-shaped association between triglyceride-glucose body mass index and short-term mortality in ICU patients with sepsis-associated acute kidney injury.docx حسب Heping Xu (95297)

"…</p>Methods<p>We conducted a retrospective analysis of ICU patients with SA-AKI using data from the MIMIC-IV database. The Boruta algorithm was employed to select significant features for predicting short-term mortality in SA-AKI patients. …"

Table 1_L-shaped association between triglyceride-glucose body mass index and short-term mortality in ICU patients with sepsis-associated acute kidney injury.docx حسب Heping Xu (95297)

"…</p>Methods<p>We conducted a retrospective analysis of ICU patients with SA-AKI using data from the MIMIC-IV database. The Boruta algorithm was employed to select significant features for predicting short-term mortality in SA-AKI patients. …"

Table 1_Evaluating the role of insulin resistance in chronic intestinal health issues: NHANES study findings.docx حسب Dongyao Zhao (21422942)

"…Key variables were selected via the Boruta algorithm and incorporated into weighted multivariate logistic regression models. …"

4D water droplet collision datasets حسب Yuhe Zhang (18089881)

"…<p dir="ltr">We share the simulated water droplet collision datasets modeled with the Navier-Stokes Cahn-Hilliard equations. This data is used to validate our deep-learning 4D reconstruction algorithm, as reported in "4D-ONIX for reconstructing 3D movies from sparse X-ray projections via deep learning" [1]. …"

Image 6_Differentiating pancreatic ductal adenocarcinoma and autoimmune pancreatitis using a machine learning model based on ultrasound clinical features.jpeg حسب Lihua Zhang (22614)

"…Purpose<p>This study aimed to construct a differential diagnostic model to distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDCA) using ultrasound clinical features and machine learning algorithms.</p>Methods<p>Retrospective ultrasound clinical data of patients with AIP and PDCA from three different centers were used as the training cohort, external validation cohort 1, and external validation cohort 2. …"

Image 3_Differentiating pancreatic ductal adenocarcinoma and autoimmune pancreatitis using a machine learning model based on ultrasound clinical features.jpeg حسب Lihua Zhang (22614)

"…Purpose<p>This study aimed to construct a differential diagnostic model to distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDCA) using ultrasound clinical features and machine learning algorithms.</p>Methods<p>Retrospective ultrasound clinical data of patients with AIP and PDCA from three different centers were used as the training cohort, external validation cohort 1, and external validation cohort 2. …"

Image 7_Differentiating pancreatic ductal adenocarcinoma and autoimmune pancreatitis using a machine learning model based on ultrasound clinical features.jpeg حسب Lihua Zhang (22614)

"…Purpose<p>This study aimed to construct a differential diagnostic model to distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDCA) using ultrasound clinical features and machine learning algorithms.</p>Methods<p>Retrospective ultrasound clinical data of patients with AIP and PDCA from three different centers were used as the training cohort, external validation cohort 1, and external validation cohort 2. …"

Image 8_Differentiating pancreatic ductal adenocarcinoma and autoimmune pancreatitis using a machine learning model based on ultrasound clinical features.jpeg حسب Lihua Zhang (22614)

"…Purpose<p>This study aimed to construct a differential diagnostic model to distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDCA) using ultrasound clinical features and machine learning algorithms.</p>Methods<p>Retrospective ultrasound clinical data of patients with AIP and PDCA from three different centers were used as the training cohort, external validation cohort 1, and external validation cohort 2. …"

Image 1_Differentiating pancreatic ductal adenocarcinoma and autoimmune pancreatitis using a machine learning model based on ultrasound clinical features.jpeg حسب Lihua Zhang (22614)

"…Purpose<p>This study aimed to construct a differential diagnostic model to distinguish autoimmune pancreatitis (AIP) from pancreatic ductal adenocarcinoma (PDCA) using ultrasound clinical features and machine learning algorithms.</p>Methods<p>Retrospective ultrasound clinical data of patients with AIP and PDCA from three different centers were used as the training cohort, external validation cohort 1, and external validation cohort 2. …"