Illustration of the wAL GHAC algorithm on a small network. by Petr Prokop (19959315)

The multiple factors influencing carbon price. by Beibei Hu (5347559)

Table_1_Preoperative prediction of tumor budding in rectal cancer using multiple machine learning algorithms based on MRI T2WI radiomics.docx by Xueting Qu (16980670)

DataSheet_1_A machine learning-based model for predicting distant metastasis in patients with rectal cancer.docx by Binxu Qiu (14280800)

“…</p>Conclusion<p>The study developed and validated an XGB model based on clinicopathological information for predicting the risk of distant metastasis in patients with rectal cancer, which may help physicians make clinical decisions. rectal cancer, distant metastasis, web calculator, machine learning algorithm, external validation</p>…”

Table_1_A machine learning-based model for predicting distant metastasis in patients with rectal cancer.docx by Binxu Qiu (14280800)

“…</p>Conclusion<p>The study developed and validated an XGB model based on clinicopathological information for predicting the risk of distant metastasis in patients with rectal cancer, which may help physicians make clinical decisions. rectal cancer, distant metastasis, web calculator, machine learning algorithm, external validation</p>…”

Table_2_A machine learning-based model for predicting distant metastasis in patients with rectal cancer.docx by Binxu Qiu (14280800)

“…</p>Conclusion<p>The study developed and validated an XGB model based on clinicopathological information for predicting the risk of distant metastasis in patients with rectal cancer, which may help physicians make clinical decisions. rectal cancer, distant metastasis, web calculator, machine learning algorithm, external validation</p>…”

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

Quantitative Polypharmacology Profiling Based on a Multifingerprint Similarity Predictive Approach by Fulvio Ciriaco (5569355)

“…We present a new quantitative ligand-based bioactivity prediction approach employing a multifingerprint similarity search algorithm, enabling the polypharmacological profiling of small molecules. …”

Quantitative Polypharmacology Profiling Based on a Multifingerprint Similarity Predictive Approach by Fulvio Ciriaco (5569355)

“…We present a new quantitative ligand-based bioactivity prediction approach employing a multifingerprint similarity search algorithm, enabling the polypharmacological profiling of small molecules. …”

Table 1_Ensemble machine learning for predicting renal function decline in chronic kidney disease: development and external validation.docx by Hong Chen (108084)

“…</p>Methods<p>We developed an ensemble machine learning model using Random Forest, XGBoost, and LightGBM algorithms, incorporating advanced feature selection and hyperparameter tuning. …”

Flowchart of this study. by Guanghao Xin (17583227)

“…Four Pd hub genes were obtained after processing by differential expression analysis, WGCNA and two machine learning algorithms. A diagnostic model for PD was constructed using these hub genes, and the genes and model were verified to have some accuracy using three independent external datasets. …”

Table 2_An explainable machine learning model for predicting preterm birth in pregnant women with gestational diabetes mellitus and hypertensive disorders of pregnancy: development... by Landan Kang (20108214)

“…</p>Methods<p>This retrospective dual-center study included electronic medical records from 121 and 136 pregnant women with comorbid GDM and HDP, which served as the development and external validation cohorts, respectively. Multiple machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) regression, Random Forest (RF), and Naive Bayes (NB), were applied to construct predictive models. …”

Table 1_An explainable machine learning model for predicting preterm birth in pregnant women with gestational diabetes mellitus and hypertensive disorders of pregnancy: development... by Landan Kang (20108214)

“…</p>Methods<p>This retrospective dual-center study included electronic medical records from 121 and 136 pregnant women with comorbid GDM and HDP, which served as the development and external validation cohorts, respectively. Multiple machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO) regression, Random Forest (RF), and Naive Bayes (NB), were applied to construct predictive models. …”

Image_1_Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma.pdf by Pengpeng Zhang (583380)

“…Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. …”

Table_1_Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma.docx by Pengpeng Zhang (583380)

“…Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. …”

Data Sheet 2_Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments.zip by Yonghua Pang (20998022)

“…Additionally, the GSE68776 dataset was used for external validation. To identify key diagnostic genes, we applied three machine learning algorithms: least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF).…”

Data Sheet 1_Identification and validation of HOXC6 as a diagnostic biomarker for Ewing sarcoma: insights from machine learning algorithms and in vitro experiments.zip by Yonghua Pang (20998022)

“…Additionally, the GSE68776 dataset was used for external validation. To identify key diagnostic genes, we applied three machine learning algorithms: least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF).…”

DataSheet_1_The development of a quantification method for measuring iridescence using sexually selected traits in the Gulf pipefish (Syngnathus scovelli).docx by Nicole M. Tosto (15325744)

Predicting Chemical Immunotoxicity through Data-Driven QSAR Modeling of Aryl Hydrocarbon Receptor Agonism and Related Toxicity Mechanisms by Nada J. Daood (18626266)

“…A total of 20 assays were further selected based on QSAR model performance, and their resulting QSAR models showed good predictivity of potential immunotoxicants from external chemicals. …”

Table3_Machine learning-based identification of a novel prognosis-related long noncoding RNA signature for gastric cancer.XLSX by Linli Zhao (14103669)

“…Our findings provide ideas for integrating multiple screening methods for risk modeling through machine learning algorithms.…”